ptm = proc.time()

1 Introdução

Frequentemente utilizamos serviços de classificação, seja com o intuito de recomendar/sugerir algo a um usuário final, como Netflix, Mercado Livre, OLX dentre outros, ou seja para determinar em testes de diagnóstico de doenças se um paciente deve ser classificado como doente, não doente ou suspeito. Além de outros exemplos. O conjunto de dados explanados a seguir foram extraídos da plataforma Kaggle e são referentes ao catálogo de filmes da plataforma IMDB, o intuito aqui é criar um sistema de classificação de estrelas do catálogo de avaliação de filmes do IMDB.

Esse projeto irá explorar o conjunto de dados a fim de gerar insights, e aplicar o algoritmo de Random Forest - RF (baseado na clusterização de Breiman) para classificação de filmes nas seguintes faixas/categorias de estrelas (Rating):

Para este, se faz necessário um largo conjunto de dados que, por sua vez, deve ser bem estruturado e consistente. Portanto, é fundamental manipular, limpar e remover informações faltantes dos dados para a implementação do modelo de Machine Learning do Random Forest que será treinado com orientação a esses dados.

Além disso, esse trabalho irá levantar quais os fatores mais importantes para que um filme tenha uma alta classificação (categoria de faixas de estrelas), via modelagem de Random Forest, de tal forma que essas mesmas variáveis/fatores destacados como importantes no algoritmo do RF possam ser aproveitados posteriormente em análises futuras de classificação. Resultados e script foram gerados utilizando recursos e linguagens em \(R\), \(\LaTeX\) e \(Markdown\).


Pacotes e Leitura

Alguns pacotes R utilizados nessa rotina

library(tidyverse)          # Manipulação de banco de dados e análise exploratória
library(highcharter)        # visualização Gráfica
library(DT)                 # Construção de Tabelas 
library(knitr)              # Opções Rmarkdown (inclusão de tabelas, imagens, etc.)
library(kableExtra)         # Construção de Tabelas 
library(rpart.plot)         # Recursive Partitioning and Regression Trees
library(randomForest)       # Modelo Random Forest
library(corrplot)           # Matriz de correlação - Visualização gráfica
library(wordcloud2)         # Numvem de palavras (Word Cloud)
library(caret)              # Matriz de confusão (Confusion Matrix) 
library(rpart)
#devtools::install_github("ropenscilabs/icon")
library(icon)
RECOMEND.METADATA = readxl::read_xlsx(path = ".../DB/IMDB.xlsx", sheet = 1)
db = RECOMEND.METADATA

+ + =

2 Banco de Dados

O conjunto de dados utilizado foi o de título IMDB 5000 extraído da plataforma kaggle. As informações contidas no banco foram catalogadas de filmes publicados ao longo de 100 anos em 66 países (entre 1916 e 2016) da plataforma IMDB - Internet Movie DataBases, o Arquivo original contém 5044 filmes (observações) e 28 variáveis descritas a seguir.

Os dados em questão são públicos e disponíveis para download clicando AQUI.

3 Análise Exploratória dos dados

## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

3.1 Estrutura dos dados

## O banco de dados possui 5043 observações e 28 variáveis

Excluindo algumas variáveis que não serão utilizadas.

    1. As variáveis color, director_name, language, country, content_rating, em especial, foram removidas uma vez que estas não se mostraram importantes no algoritmo de importância do Random Forest de Breiman.
    1. Já as variáveis director_facebook_likes, actor_3_facebook_likes, actor_2_name, actor_1_facebook_likes, actor_1_name, actor_3_name,-facenumber_in_poster, actor_2_facebook_likes, movie_imdb_link foram extraídas de forma determinística por escolha dos autores deste.

O motivo de remover as variáveis em (1) é devido à tentativa de reduzir o número de missing na base de dados. no passo 3.1 de Tratamento de NA’s.

#db=RECOMMEND.METADATA
db = db %>% select(- director_facebook_likes, -actor_3_facebook_likes, -actor_2_name, -actor_1_facebook_likes, -actor_1_name, -actor_3_name,-facenumber_in_poster, -actor_2_facebook_likes, -color, -director_name, -language, -country, -content_rating, -movie_imdb_link)
## O banco de dados agora possui 14 variáveis. Ou seja, foram removidas  14 variáveis no passo anterior.

Estrutura dos dados

glimpse(db) #str(db)
## Observations: 5,043
## Variables: 14
## $ num_critic_for_reviews    <int> 723, 302, 602, 813, NA, 462, 392, 32...
## $ duration                  <int> 178, 169, 148, 164, NA, 132, 156, 10...
## $ gross                     <int> 760505847, 309404152, 200074175, 448...
## $ genres                    <fct> Action|Adventure|Fantasy|Sci-Fi, Act...
## $ movie_title               <fct> Avatar , Pirates of the Caribbean: A...
## $ num_voted_users           <int> 886204, 471220, 275868, 1144337, 8, ...
## $ cast_total_facebook_likes <int> 4834, 48350, 11700, 106759, 143, 187...
## $ plot_keywords             <fct> avatar|future|marine|native|parapleg...
## $ num_user_for_reviews      <int> 3054, 1238, 994, 2701, NA, 738, 1902...
## $ budget                    <dbl> 237000000, 300000000, 245000000, 250...
## $ title_year                <int> 2009, 2007, 2015, 2012, NA, 2012, 20...
## $ imdb_score                <fct> 7.9, 7.1, 6.8, 8.5, 7.1, 6.6, 6.2, 7...
## $ aspect_ratio              <fct> 1.78, 2.35, 2.35, 2.35, , 2.35, 2.35...
## $ movie_facebook_likes      <int> 33000, 0, 85000, 164000, 0, 24000, 0...

3.2 Transformação de variáveis

Transformando as variáveis imdb_score e aspect_ratio em numéricas

db$imdb_score = as.numeric(as.character(db$imdb_score))
db$aspect_ratio = as.numeric(as.character(db$aspect_ratio))
hc1 = hchart(db$imdb_score, color = "#e8bb0b", name = "imdb_score") %>% 
        hc_title(text = "Histograma dos votos na plataforma IMDB") %>%
        hc_exporting(enabled = TRUE, filename = "Fig1-Pimenta"); hc1
hc2 = hchart(db$num_voted_users, color = "#786eea", name = "imdb_num_votos") %>% 
        hc_title(text = "Histograma dos número de votos na plataforma IMDB") %>%
        hc_exporting(enabled = TRUE, filename = "Fig2-Pimenta"); hc2

Criando uma função para cálculo da moda

Moda <- function(x) {
     ux <- unique(x)
     ux[which.max(tabulate(match(x, ux)))]
}
## Média, moda e mediana da variável imdb_score são, respectivamente, 6.44 6.7 6.6

Extraindo valores da variável gênero e transformando em dummies

gg <- as.character(db$genres); gg <- gsub("-", "_", as.character(gg))

#t <- unlist(strsplit(gg[1],split = "\\|"))
tem1 <- data.frame() 
for(i in 1:length(gg)){
        tem <- tem1
        t <- unlist(strsplit(gg[i],split = "\\|"))
        temp <- data.frame(t)
        tem1 <- rbind(tem,temp)
}

Gen <- unique(tem1); Gen <- gsub("-", "_", as.character(Gen$t))
cat("Existem", length(Gen), "valores de gêneros únicos de filme no banco de dados.")
## Existem 26 valores de gêneros únicos de filme no banco de dados.
Genname <- Gen

fe <- matrix(data = 0, nrow = length(gg), ncol = length(Genname))
fe <- data.frame(fe); colnames(fe) <- Genname

i=j=0
for(i in 1:length(gg)){
        for(j in 1:length(Genname)){
                g <- grepl(Genname[j], gg[i])
                if(g == TRUE){
                        fe[i, j] <- 1        
                }
        }
}

NumGen = as_tibble(rbind(apply(fe,2,sum)))
NumGen = gather(NumGen, key = "variables", value = "num_gender")
NumGen = NumGen[order(NumGen$num_gender, decreasing = TRUE), ]


hc3 <- highchart() %>%
  hc_add_series(data = NumGen$num_gender, 
                type = "bar",
                name = "# de filmes",
                showInLegend = FALSE,
                tooltip = list(valueDecimals = 0, valuePrefix = "", valueSuffix = ""), color="blue") %>%
  hc_yAxis(title = list(text = "Quantitativo de filmes"), 
           allowDecimals = TRUE, max = (max(NumGen$num_gender)+103),
           labels = list(format = "{value}")) %>%
  hc_xAxis(title = list(text = "Gênero de filme"),
           categories = NumGen$variables,
           tickmarkPlacement = "on",
           opposite = FALSE) %>%
  hc_title(text = "Quantitativo de filmes por gênero",
           style = list(fontWeight = "bold")) %>% 
  hc_subtitle(text = paste("")) %>%
      hc_tooltip(valueDecimals = 2,
                 pointFormat = "{point.y} filmes")%>%
                 #pointFormat = "Variável: {point.x} <br> Missing: {point.y}") 
      hc_credits(enabled = TRUE, 
                 text = "Fonte: IMDB/KAGGLE. Elaboração: Ewerson Pimenta.",
                 style = list(fontSize = "10px")) %>%
  hc_exporting(enabled = TRUE, filename = "F3-filmes-genero-Pimenta")
#hc <- hc %>% 
#  hc_add_theme(hc_theme_darkunica())
hc3; remove(gg,t,tem, temp, tem1,Gen, g, i, j)

Algumas variáveis de gêneros possuem pouquíssimos filmes serão removidas do banco. São elas Film_Noir, Short, News, Reality_TV e Game_Show.

fe$Film_Noir <- NULL
fe$Short <- NULL
fe$News <- NULL
fe$Reality_TV <- NULL
fe$Game_Show <- NULL

Unificando a base de dados com as novas variáveis de gêneros únicos descobertos.

db1 = as_tibble(cbind(as.data.frame(db),fe))
cat("Foram inseridas ", dim(fe)[2],"novas variáveis provenientes dos gêneros únicos descobertos no passo anterior.")
## Foram inseridas  21 novas variáveis provenientes dos gêneros únicos descobertos no passo anterior.

Semelhante à variável de gênero, foi feito o split por palavra chave da variável keyword, esse, por sua vez, continha 6780 palavras únicas. E então, geramos uma nuvem de palavras chave, com o auxílio do pacote wordcloud2 com o seguinte comando, após obter o data frame do nome das palavras chaves e frequência.

library(wordcloud2)
wordcloud2(NumKeyWord)

3.3 Filtrando filmes a partir de 1980

Excluindo filmes lançados antes de 1980

hc00 = hchart(db$title_year, color = "#53a074", name = "imdb1_score") %>% 
        hc_title(text = "Histograma do ano de publicação dos filmes") %>%
        hc_exporting(enabled = TRUE, filename = "Fig1-Pimenta"); hc00

Percebemos pelo gráfico anterior que existem poucos filmes publicados antes de 1980 e estes podem não ser representativos. Decidimos, então, por trabalhar apenas com os filmes que foram publicados a aprtir de 1980

db <- db[db$title_year >= 1980,]
hc01 = hchart(db$title_year, color = "#79d8a2", name = "imdb1_score") %>% 
        hc_title(text = "Histograma do ano de publicação dos filmes") %>%
        hc_exporting(enabled = TRUE, filename = "Fig1-Pimenta"); hc01

3.4 Tratamento de NA, zeros e duplicatas

Porcentagem de NA por variável

db_miss <- db %>% summarise_all(funs(sum(is.na(.))/n()))
## Warning: package 'bindrcpp' was built under R version 3.5.1
db_miss <- gather(db_miss, key = "variables", value = "percent_missing")
db_miss$percent_missing = 100*db_miss$percent_missing
db_miss = db_miss[order(db_miss$percent_missing, decreasing = TRUE), ]
#db_miss

hc4 <- highchart() %>%
  hc_add_series(data = db_miss$percent_missing, 
                type = "bar",
                name = "Porcentagem de missing",
                showInLegend = FALSE,
                tooltip = list(valueDecimals = 2, valuePrefix = "", valueSuffix = " %")) %>%
  hc_yAxis(title = list(text = "Porcentagem de missing"), 
           allowDecimals = TRUE, max = 20,
           labels = list(format = "{value}%")) %>%
  hc_xAxis(categories = db_miss$variables,
           tickmarkPlacement = "on",
           opposite = FALSE) %>%
  hc_title(text = "Porcentagem de missinig por variável",
           style = list(fontWeight = "bold")) %>% 
  hc_subtitle(text = paste("")) %>%
      hc_tooltip(valueDecimals = 2,
                 pointFormat = "Missing: {point.y}")%>%
                 #pointFormat = "Variável: {point.x} <br> Missing: {point.y}") 
      hc_credits(enabled = TRUE, 
                 text = "Fonte: IMDB/KAGGLE. Elaboração: Ewerson Pimenta.",
                 style = list(fontSize = "10px")) %>%
  hc_exporting(enabled = TRUE, filename = "Fig0-Pimenta")
#hc <- hc %>% 
#  hc_add_theme(hc_theme_darkunica())
hc4

Removendo todas as observações que contêm NA.

db1 <- db1 %>% drop_na(gross, budget, aspect_ratio 
,title_year 
,num_critic_for_reviews, num_user_for_reviews)
#glimpse(db1)

db1 <- db1 %>% na.omit()
db1 <- db1 %>% drop_na()

Removendo dados duplicados

cat("Existem", sum(duplicated(db1)), "filmes duplicados na base de dados.")
## Existem 33 filmes duplicados na base de dados.
db1 <- db1[!duplicated(db1), ]
## O novo banco de dados, sem observações faltantes, possui 3782 observações. Ou seja, no processo de remoção de valores faltantes foram perdidas 1000 observações. Finalmente, removemos todas as observações duplicadas e faltantes do banco.

Porcentagem de ZEROS por variável

zeros <- (colSums(db1==0)/nrow(db1)*100); var <- names(zeros)
db_zero <- data.frame(var,zeros); rownames(db_zero) <- NULL
db_zero <- db_zero[order(db_zero$zeros, decreasing = TRUE), ]

hc4_1 <- highchart() %>%
  hc_add_series(data = db_zero$zeros, 
                type = "bar",
                name = "Porcentagem de zeros",
                showInLegend = FALSE,
                tooltip = list(valueDecimals = 2, valuePrefix = "", valueSuffix = " %"), color="pink") %>%
  hc_yAxis(title = list(text = "Porcentagem de zero"), 
           allowDecimals = TRUE, max = 100,
           labels = list(format = "{value}%")) %>%
  hc_xAxis(categories = db_zero$var,
           tickmarkPlacement = "on",
           opposite = FALSE) %>%
  hc_title(text = "Porcentagem de zeros por variável",
           style = list(fontWeight = "bold")) %>% 
  hc_subtitle(text = paste("")) %>%
      hc_tooltip(valueDecimals = 2,
                 pointFormat = "Zeros: {point.y}")%>%
                 #pointFormat = "Variável: {point.x} <br> Missing: {point.y}") 
      hc_credits(enabled = TRUE, 
                 text = "Fonte: IMDB/KAGGLE. Elaboração: Ewerson Pimenta.",
                 style = list(fontSize = "10px")) %>%
  hc_exporting(enabled = TRUE, filename = "Fig00-Pimenta")
#hc <- hc %>% 
#  hc_add_theme(hc_theme_darkunica())
hc4_1

Aqui podemos perceber que os zeros estão concentrados nas variáveis de gênero que são binárias e não faz sentido analizar os zeros nessas variáveis.

paste0("Entretanto, pode-se perceber uma proporção de zeros na variável movie_facebook_likes igual a ",round(db_zero[22,2],2), "% e, também na variável cast_total_facebook_likes de ",round(db_zero[23,2],2),"%. Decidimos por remover a variável movie_facebook_likes.")
## [1] "Entretanto, pode-se perceber uma proporção de zeros na variável movie_facebook_likes igual a 46.17% e, também na variável cast_total_facebook_likes de 0.13%. Decidimos por remover a variável movie_facebook_likes."
db1$movie_facebook_likes <- NULL

3.5 Weighted Rating (WR) - IMDB_score

Um passo importante é penalizar a variável de escore imdb_score pelo número de votos recebidos. Ver mais no estimador de Shrinkage

\(WR = \frac{v}{v+m} \times R + \frac{m}{v+m} \times C\)

Onde,

R = as.numeric(db1$imdb_score)
v = as.numeric(db1$num_voted_users)
m = summary(db1$num_voted_users)[2] # 1st Qu.
C = mean(db1$imdb_score)
db1$WR = (v/(v+m))*R + (m/(v+m))*C
  • R = Escore médio dos votos para o título do filme dado pelos usuários do IMDB = (imdb_score)
  • v = Número de usuários que votaram = (num_voted_users)
  • m = Mínimo de votos requerido (atualmente 7.000)
  • C = O escore médio de todos os 3766 filmes (atualmente 6,5)

Medidas pontuais e de dispersão de imdb_score e WR

summary(db1$imdb_score)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.600   5.900   6.600   6.468   7.200   9.300
summary(db1$WR)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.578   6.166   6.498   6.573   6.983   9.269
cat("Além disso, conseguimos reduzir o desvio padrão que orignalmente era de ",round(sd(db1$imdb_score),2),",para a variável imdb_score, e agora passou a ser ",round(sd(db1$WR),2), "para a variável WR.")
## Além disso, conseguimos reduzir o desvio padrão que orignalmente era de  1.05 ,para a variável imdb_score, e agora passou a ser  0.72 para a variável WR.

Selecionando uma amostra aleatória (a.a.) de tamanho \(n=800\) e representando em um gráfico de dispersão com valores reais vs ajustados.

set.seed(123654)
amostra0 = sample(x = 1:dim(db1)[1], size = 800, replace = FALSE)
dbX = db1[amostra0,] %>% 
  select(movie_title,num_voted_users,imdb_score, WR)
  
dss <- map(c("cross"), function(s){
  
  x <- as.numeric(dbX$imdb_score)
  y <- as.numeric(dbX$WR)
  
  list(name = s,
       data = list_parse(data_frame(x, y)),
       marker = list(symbol = s, enabled = TRUE), lineColor = "#56667a")
  
})
#dss[[1]]$data[amostra1]

hc5 = highchart() %>% 
  hc_chart(type = "scatter", color = "#56667a") %>% 
  hc_title(text = "Score IMDB vs WR (calculado pelo estimador de Shrinkage)") %>%
  hc_subtitle(text = "800 filmes selecionados via amostra aleatória simples") %>%
  hc_xAxis(title = list(text = "x: imdb_score"), 
           allowDecimals = TRUE, labels = list(format = "{value}★")) %>%
  hc_yAxis(title = list(text = "y: WR (calibrado)"),
           allowDecimals = TRUE, labels = list(format = "{value}★")) %>%
  hc_exporting(enabled = TRUE, filename = "F3-Pimenta") %>%
  hc_add_series_list(dss); hc5; remove(dbX, dss)

Análise do cálculo de IMDB

  • Para filmes com # de votos recebidos MENOR que 18mil (m: votos mínimos requeridos):
    • imdb_score > 6,5: DECRESCIMENTO
    • imdb_score < 6,5: CRESCIMENTO
  • Para filmes com # de votos recebidos MAIOR que 18mil (m: votos mínimos requeridos):
    • imdb_score > 6,5: DECRESCIMENTO
    • imdb_score < 6,5: CRESCIMENTO

Ou seja, para os filmes catalogados com m muito inferior a 18mil o novo escore calibrado teve maior diferentça que aqueles superior a 18 mil. Além disso, quando scores são maiores que 6,5 o WR tende a cair, caso contrário o valor pode aumentar. Quanto maior o número de votos recebidos, menor a diferença do valor de IMDB_score e WR.

set.seed(123654)
amostra2 = sample(x = 1:dim(db1)[1], size = 35, replace = FALSE)
db1[amostra2,] %>% 
  select(movie_title,num_voted_users,imdb_score, WR, budget) %>% 
  datatable(rownames = amostra2,options = list(searchin = TRUE, scrollX = TRUE, pageLength = 5))

3.6 Visualizações Finais

Finalmente, segue uma a.a.da base de dados após limpeza e análise exploratória.

set.seed(123851)
amostra3 = sample(x = 1:dim(db1)[1], size = 35, replace = FALSE)
db1[amostra3,] %>%
datatable(rownames = amostra3,options = list(searchin = TRUE, scrollX = TRUE, pageLength = 5))

Para os dados quantitativos a seguinte Matriz de correlação. Foi gerada utilizando o pacote corrplot

#install.packages("corrplot")
library(corrplot)  
res1 <- cor.mtest(db_cor, conf.level = .95)
res2 <- cor.mtest(db_cor, conf.level = .99)

corrplot::corrplot(cor(db_cor), method = "color", col = col(200),
         type = "upper", order = "hclust", number.cex = .7,
         addCoef.col = "black", # Add coefficient of correlation
         tl.col = "black", tl.srt = 90, # Text label color and rotation
         # Combine with significance
         p.mat = res1$p, sig.level = 0.01, insig = "blank", 
         # hide correlation coefficient on the principal diagonal
         diag = FALSE)



4 Modelagem

4.1 Random Forest - Metodologia

Descrição 1. Random Forest foi desenvolvido para agregar árvores de decisão (modelo de classificação);
2. Pode ser usado para modelo de classificação (p/ var. resposta categórica) ou regressão (no caso de haver variável resposta contínua);
3. Evita overfitting;
4. Permite trabalhar com um largo número de características de um conjunto de dados;
5. Auxilia na seleção de variáveis baseada em um algoritmo que calcula a importância por variável (assim, tendo conhecimento de quais variáveis são mais importantes, podemos usar essa informação para outros modelos de classificação);
6. User-friendly: apenas 2 parâmetros livres:

  • Trees - ntrees, default 500 (Nº de árvores);
  • Variáveis selecionadas via amostragem aleatória candidatas à cada “split” (quebra da árvore) - mtry, default \(\sqrt{p}\) p/ classificação e \(\frac{p}{3}\) p/ regressão (p: nº de features/variáveis);

Passo-a-Passo

É realizado em 3 passos:

  1. Desenha as amostras via bootstrap do número de árvores ntrees;
  2. Para cada amostra via bootstrap, cresce o número de árvores “un-puned” para a escolha da melhor quebra da árvore baseado na amostra aleatória do valor predito de mtry a cada nó da árvore;
    1. Faz classificação de novos valores usando a maioria de votos p/ classificação e usa a média p/ regressão baseada nas amostras de ntrees.

Exemplo

4.2 Preparação de dados

Plot da variável imdb_score

plot(db1$imdb_score, ylim = c(0,10))

hist(db1$imdb_score)

Plot da variável WR (imdb_score penalizada)

plot(db1$WR, ylim = c(0,10))

hist(db1$WR)

dist1 = 2.5
db2 = db1 %>% filter(WR >= (mean(db1$WR)- dist1) & WR <= (mean(db1$WR)+ dist1))
paste('Excluímos ', dim(db1)[1] - dim(db2)[1], ' filmes da base. O critério aqui utilizado foi o de permanecer, apenas, filmes distantes em até ', dist1, ' estrelas da média de estrelas ',round(mean(db1$WR),2),'.')
## [1] "Excluímos  16  filmes da base. O critério aqui utilizado foi o de permanecer, apenas, filmes distantes em até  2.5  estrelas da média de estrelas  6.57 ."

Transformação da variável contínua WRem categórica WR_Grp

Transformando a variável WR em fator, com as seguintes categorias:

  • Categoria ‘Baixo índice de aprovação (low score)’: [0, 6.5) ★
  • Categoria ‘Alto índice de aprovação (high score)’: [6.5, 10) ★
movie = db2
Grp <- function(tn){
  tn = abs(tn)
    if (tn >= 0 & tn < median(db2$WR)){
        return(paste0('[0, ',round(median(db2$WR),2),')'))
    }else if(tn >= median(db2$WR)){
        return(paste0('[',round(median(db2$WR),2),', 10]'))
    }
}
# apply the Group function to the WR column
movie$WR_Grp <- sapply(movie$WR,Grp)
# set as factor the new column
movie$WR_Grp <- as.factor(movie$WR_Grp)
#View(head(movie))
table(movie$WR_Grp)
## 
##  [0, 6.5) [6.5, 10] 
##      1883      1883
# apply the Group function to the WR column
imdb_score_Grp <- sapply(movie$imdb_score,Grp)
# set as factor the new column
imdb_score_Grp <- as.factor(imdb_score_Grp)
#View(head(movie))
table(imdb_score_Grp)
## imdb_score_Grp
##  [0, 6.5) [6.5, 10] 
##      1696      2070
paste('O critério de seleção do valor de dist')
## [1] "O critério de seleção do valor de dist"

Remoção de variáveis Remove as variáveis imdb_score, genres, plot_keywords, movie_imdb_link e WR.

movie$imdb_score <- NULL
movie$genres <- NULL
movie$plot_keywords <- NULL
movie$movie_imdb_link <- NULL
movie$WR <- NULL
glimpse(movie)
## Observations: 3,766
## Variables: 32
## $ num_critic_for_reviews    <int> 723, 302, 602, 813, 462, 392, 324, 6...
## $ duration                  <int> 178, 169, 148, 164, 132, 156, 100, 1...
## $ gross                     <int> 760505847, 309404152, 200074175, 448...
## $ movie_title               <fct> Avatar , Pirates of the Caribbean: A...
## $ num_voted_users           <int> 886204, 471220, 275868, 1144337, 212...
## $ cast_total_facebook_likes <int> 4834, 48350, 11700, 106759, 1873, 46...
## $ num_user_for_reviews      <int> 3054, 1238, 994, 2701, 738, 1902, 38...
## $ budget                    <dbl> 237000000, 300000000, 245000000, 250...
## $ title_year                <int> 2009, 2007, 2015, 2012, 2012, 2007, ...
## $ aspect_ratio              <dbl> 1.78, 2.35, 2.35, 2.35, 2.35, 2.35, ...
## $ Action                    <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, ...
## $ Adventure                 <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ Fantasy                   <dbl> 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, ...
## $ Sci_Fi                    <dbl> 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, ...
## $ Thriller                  <dbl> 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Documentary               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Romance                   <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, ...
## $ Animation                 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ Comedy                    <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ Family                    <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, ...
## $ Musical                   <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ Mystery                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, ...
## $ Western                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Drama                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ History                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Sport                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Crime                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Horror                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ War                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Biography                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Music                     <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
## $ WR_Grp                    <fct> [6.5, 10], [6.5, 10], [6.5, 10], [6....
cat("A base de dados que iremos trabalhar no modelo tem", dim(movie)[1] , "observações e ", dim(movie)[2]-1, "variáveis, sem contar a variável que contém os títulos dos filmes.")
## A base de dados que iremos trabalhar no modelo tem 3766 observações e  31 variáveis, sem contar a variável que contém os títulos dos filmes.

Partição dos dados

Particionando a base de dados em Treino e Teste, esses dois (Treino e Teste) também terão armazenos os nomes dos filmes selecionados via amostragem probabilística dos dados originais separadamente das bases de Treino e Teste.

Posteriormente, removemos os rótulos dos filmes nas bases Treino e Teste

set.seed(9182345)
ind <- sample(2, nrow(movie), replace = T, prob = c(0.7, 0.3))
train <- movie[ind==1, -4]
test <- movie[ind==2, -4]
trainMovie <- movie[ind==1, 4]
testMovie <- movie[ind==2, 4]

Distribuição dos escores nas bases de treino e teste

round(table(train$WR_Grp)/sum(table(train$WR_Grp))*100,2)
## 
##  [0, 6.5) [6.5, 10] 
##     49.79     50.21
round(table(test$WR_Grp)/sum(table(test$WR_Grp))*100,2)
## 
##  [0, 6.5) [6.5, 10] 
##      50.5      49.5

4.3 Random Forest - Aplicação e Resultados

Inicialmente utilizaremos o pacote randomForest que implmenta o algoritmo de Random Forest de Breiman (baseado na clusterização de Breiman, originalmente codificada em Fortran) que tem por finalidade classificar e/ou criar regressão. Além disso, pode ser usado em um modelo não supervisionado para avaliar proximidades entre pontos.

Estamos usando, a partir daqui, a base de treino.

#library(randomForest)
#library(rpart)
#library(rpart.plot)
#rf <- randomForest(proximity = T,ntree = 38,do.trace = T,WR~.,data=training)
set.seed(9984512)
rf <- randomForest(WR_Grp~.,data=train)
rf
## 
## Call:
##  randomForest(formula = WR_Grp ~ ., data = train) 
##                Type of random forest: classification
##                      Number of trees: 500
## No. of variables tried at each split: 5
## 
##         OOB estimate of  error rate: 18.6%
## Confusion matrix:
##           [0, 6.5) [6.5, 10] class.error
## [0, 6.5)      1093       235   0.1769578
## [6.5, 10]      261      1078   0.1949216
attributes(rf)
## $names
##  [1] "call"            "type"            "predicted"      
##  [4] "err.rate"        "confusion"       "votes"          
##  [7] "oob.times"       "classes"         "importance"     
## [10] "importanceSD"    "localImportance" "proximity"      
## [13] "ntree"           "mtry"            "forest"         
## [16] "y"               "test"            "inbag"          
## [19] "terms"          
## 
## $class
## [1] "randomForest.formula" "randomForest"

Olhando as 6 primeiras observações real X predito

p1 <- predict(rf,train)
head(p1)
##         1         2         3         4         5         6 
## [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10]  [0, 6.5) 
## Levels: [0, 6.5) [6.5, 10]
head(train$WR_Grp)
## [1] [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] [0, 6.5) 
## Levels: [0, 6.5) [6.5, 10]

Matriz de confusão

library(caret)
library(e1071)
## Warning: package 'e1071' was built under R version 3.5.1
confusionMatrix(p1, train$WR_Grp)
## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  [0, 6.5) [6.5, 10]
##   [0, 6.5)      1328         0
##   [6.5, 10]        0      1339
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9986, 1)
##     No Information Rate : 0.5021     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.4979     
##          Detection Rate : 0.4979     
##    Detection Prevalence : 0.4979     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : [0, 6.5)   
## 

RF_importance = randomForest::importance(rf)[order(randomForest::importance(rf)[,1], decreasing = TRUE), ]
knitr::kable(RF_importance)
x
num_voted_users 214.200087
duration 130.658019
budget 129.628961
num_user_for_reviews 128.904402
num_critic_for_reviews 118.778521
gross 108.488991
title_year 88.031601
cast_total_facebook_likes 86.867504
Drama 63.647354
Comedy 23.176435
aspect_ratio 19.831347
Biography 19.566187
Thriller 19.560450
Action 17.939507
Horror 17.176821
Animation 16.687173
Romance 13.200739
Crime 11.155917
Adventure 9.461303
Fantasy 9.243306
Sci_Fi 8.888087
Mystery 7.818714
Family 7.534521
History 7.081343
Music 6.440673
War 5.641061
Documentary 4.693869
Musical 4.294417
Sport 4.028837
Western 2.058234
Verificamos que as variávei Game_Show, Sci_Fi, Reality_TV, News e Film_Noir não foram relevantes para o algoritmo do random forest.
randomForest::varImpPlot(rf)

Taxa de Erro - Random Forest

plot(rf)
legend('topright', colnames(rf$err.rate), col=1:5, fill=1:5)

Observamos que, a partir do número de árvores geradas \(ntrees > 300\) o erro OOB (Out of Bag) não pode ser melhorado.

Ajustando e melhorando estimativas

Além disso, iremos alterar alguns parâmetros da função randomForest como o número de ntrees e mtry. Assim, repetimos o algoritmo do Random Forest, ainda usando a base treino.

# Tune mtry
x = as.data.frame(train1[,-31])
y = (as.factor(train1$WR_Grp))
t <- tuneRF(x = x, y = y,
       stepFactor = 0.3,
       plot = TRUE,
       ntreeTry = 300,
       trace = TRUE,
       improve = 0.05)
## mtry = 5  OOB error = 17.85% 
## Searching left ...
## mtry = 17    OOB error = 18.04% 
## -0.0105042 0.05 
## Searching right ...
## mtry = 1     OOB error = 24.45% 
## -0.3697479 0.05

Aparentemente, 5 é um bom candidato ao valor de \(m_{try}\)

#set.seed(093180)
set.seed(998451)
rf1 <- randomForest(WR_Grp~.,data=train1, 
                    ntree = 300, 
                    mtry = 5, 
                    importance = TRUE,
                    proximity = TRUE)
rf1
## 
## Call:
##  randomForest(formula = WR_Grp ~ ., data = train1, ntree = 300,      mtry = 5, importance = TRUE, proximity = TRUE) 
##                Type of random forest: classification
##                      Number of trees: 300
## No. of variables tried at each split: 5
## 
##         OOB estimate of  error rate: 18.45%
## Confusion matrix:
##           [0, 6.5) [6.5, 10] class.error
## [0, 6.5)      1105       223   0.1679217
## [6.5, 10]      269      1070   0.2008962
RF_importance1 = randomForest::importance(rf1)[order(randomForest::importance(rf1)[,1], decreasing = TRUE), ]
plot(rf1)
legend('topright', colnames(rf$err.rate), col=1:5, fill=1:5)

Removendo variáveis com pouca importância

Decidimos por retirar todos os fatores que retornaram importância abaixo de 10 em MeanDecreaseGini. Portanto, ficaremos apenas com as seguintes variáveis:

RF1 = data.frame(variables = rownames(RF_importance1), importance = RF_importance1[,4])
RF1 = RF1[order(RF1$importance, decreasing = TRUE),]
rownames(RF1) <- NULL
RF1 = RF1[1:10,]
hc6_1 <- highchart() %>%
  hc_add_series(data = RF1$importance, 
                type = "bar",
                name = "Importância",
                showInLegend = FALSE,
                tooltip = list(valueDecimals = 2, valuePrefix = "", valueSuffix = "")) %>%
  hc_yAxis(title = list(text = "Importância"), 
           allowDecimals = TRUE, max = 200,
           labels = list(format = "{value}")) %>%
  hc_xAxis(title = list(text = "Fatores"),
           categories = RF1$variables,
           tickmarkPlacement = "on",
           opposite = FALSE) %>%
  hc_title(text = "Importância por fator - Random Forest",
           style = list(fontWeight = "bold")) %>% 
  hc_subtitle(text = paste("")) %>%
      hc_tooltip(valueDecimals = 2,
                 pointFormat = "Importância: {point.y}")%>%
                 #pointFormat = "Variável: {point.x} <br> Importância: {point.y}") 
      hc_credits(enabled = TRUE, 
                 text = "Fonte: IMDB/KAGGLE. Elaboração: Ewerson Pimenta.",
                 style = list(fontSize = "10px")) %>%
  hc_exporting(enabled = TRUE, filename = "F6_1-importance-Pimenta")
#hc <- hc %>% 
#  hc_add_theme(hc_theme_darkunica())
hc6_1

Selecionando variáveis importantes

train2 = train %>% select(num_voted_users, gross, duration, num_user_for_reviews, budget, cast_total_facebook_likes, title_year, num_critic_for_reviews, Drama, Horror, aspect_ratio, Action, Comedy, WR_Grp) %>% droplevels()
test2 = test %>% select(num_voted_users, gross, duration, num_user_for_reviews, budget, cast_total_facebook_likes, title_year, num_critic_for_reviews, Drama, Horror, aspect_ratio, Action, Comedy, WR_Grp) %>% droplevels()
movie = rbind(train2, test2)
# Tune mtry
x = as.data.frame(train2[,-14])
y = (as.factor(train2$WR_Grp))
t <- tuneRF(x = x, y = y,
       stepFactor = .7,
       plot = TRUE,
       ntreeTry = 150,
       trace = TRUE,
       improve = 0.05)
## mtry = 3  OOB error = 20.25% 
## Searching left ...
## mtry = 5     OOB error = 20.32% 
## -0.003703704 0.05 
## Searching right ...
## mtry = 2     OOB error = 20.85% 
## -0.02962963 0.05

Modelo Final Os parâmetros utilizados, finalmente serão \(m_{try} = 3\) e \(n_{tree} = 150\)

set.seed(093180)
rf_final <- randomForest(WR_Grp~.,data = train2,
                    ntree = 150, 
                    mtry = 3, 
                    importance = TRUE,
                    proximity = TRUE)
rf_final; #attributes(rf_final)
## 
## Call:
##  randomForest(formula = WR_Grp ~ ., data = train2, ntree = 150,      mtry = 3, importance = TRUE, proximity = TRUE) 
##                Type of random forest: classification
##                      Number of trees: 150
## No. of variables tried at each split: 3
## 
##         OOB estimate of  error rate: 20.02%
## Confusion matrix:
##           [0, 6.5) [6.5, 10] class.error
## [0, 6.5)      1089       239   0.1799699
## [6.5, 10]      295      1044   0.2203137
RF_importance1 = randomForest::importance(rf_final)[order(randomForest::importance(rf_final)[,1], decreasing = TRUE), ]
plot(rf_final)
legend('topright', colnames(rf$err.rate), col=1:5, fill=1:5)

Predição e matriz de confusão - train data

library(caret)
p1 <- predict(rf_final,train2)
head(p1)
##         1         2         3         4         5         6 
## [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10]  [0, 6.5) 
## Levels: [0, 6.5) [6.5, 10]
head(train2$WR_Grp)
## [1] [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] [0, 6.5) 
## Levels: [0, 6.5) [6.5, 10]
confusionMatrix(p1, train2$WR_Grp)
## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  [0, 6.5) [6.5, 10]
##   [0, 6.5)      1328         0
##   [6.5, 10]        0      1339
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9986, 1)
##     No Information Rate : 0.5021     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.4979     
##          Detection Rate : 0.4979     
##    Detection Prevalence : 0.4979     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : [0, 6.5)   
## 

Predição e matriz de confusão - test data

p2 <- predict(rf_final,test2)
head(p2)
##         1         2         3         4         5         6 
## [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10] 
## Levels: [0, 6.5) [6.5, 10]
head(test2$WR_Grp)
## [1] [6.5, 10] [0, 6.5)  [6.5, 10] [6.5, 10] [6.5, 10] [6.5, 10]
## Levels: [0, 6.5) [6.5, 10]
confusionMatrix(p2, test2$WR_Grp)
## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  [0, 6.5) [6.5, 10]
##   [0, 6.5)       469       105
##   [6.5, 10]       86       439
##                                           
##                Accuracy : 0.8262          
##                  95% CI : (0.8025, 0.8482)
##     No Information Rate : 0.505           
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.6523          
##  Mcnemar's Test P-Value : 0.1928          
##                                           
##             Sensitivity : 0.8450          
##             Specificity : 0.8070          
##          Pos Pred Value : 0.8171          
##          Neg Pred Value : 0.8362          
##              Prevalence : 0.5050          
##          Detection Rate : 0.4268          
##    Detection Prevalence : 0.5223          
##       Balanced Accuracy : 0.8260          
##                                           
##        'Positive' Class : [0, 6.5)        
## 

Nº de nós nas árvores

hist(treesize(rf_final),
     main = "Nº de nós por ávore",
     col = "green")

Extração de uma única árvore \(Árvore \space n_{tree}=1\)

datatable(getTree(rf_final, 1, labelVar = TRUE),  
          options = list(searchin = TRUE, pageLength = 5))

\(Árvore \space n_{tree}=149\)

datatable(getTree(rf_final, 149, labelVar = TRUE),  
          options = list(searchin = TRUE, pageLength = 5))

Gráfico de escala multidimensional da matriz de proximidade Classical multidimensional scaling (MDS) of a data matrix. Also known as principal coordinates analysis (Gower, 1966).

Note that because of numerical errors the computed eigenvalues need not all be non-negative, and even theoretically the representation could be in fewer than n - 1 dimensions.

(MDIM = MDSplot(rf_final, train2$WR_Grp, pch=20))
## Warning in RColorBrewer::brewer.pal(nlevs, "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

## $points
##              Dim 1         Dim 2
## 1     1.191416e-01 -6.186093e-02
## 2     1.142484e-01 -6.157681e-02
## 3     8.108504e-02 -2.106161e-02
## 4     9.144692e-02 -4.227019e-02
## 5     2.663782e-02  2.323193e-02
## 6     1.892676e-01 -1.251081e-01
## 7     5.039433e-02  3.354186e-03
## 8     1.433929e-01 -8.183954e-02
## 9     1.017028e-01 -4.894726e-02
## 10    2.521623e-02  1.940811e-02
## 11    1.476662e-01 -8.933971e-02
## 12    4.361238e-02  1.008992e-02
## 13    2.014302e-01 -1.319519e-01
## 14    1.228034e-03  4.991470e-02
## 15    1.643644e-01 -1.035877e-01
## 16    1.189432e-01 -5.776495e-02
## 17    3.676391e-02  1.861320e-02
## 18    1.396095e-01 -7.457756e-02
## 19    4.480818e-02  1.136645e-02
## 20    1.366275e-01 -7.233714e-02
## 21   -3.917364e-03  5.210435e-02
## 22    6.662793e-02 -1.345253e-02
## 23    5.591800e-02 -9.949281e-04
## 24    4.410363e-02  1.524389e-02
## 25    1.377117e-01 -7.932194e-02
## 26    1.508044e-01 -9.082310e-02
## 27    7.276718e-03  4.009574e-02
## 28    5.614959e-02 -6.371171e-03
## 29   -2.083999e-03  4.503514e-02
## 30   -1.471098e-02  5.268769e-02
## 31    1.328977e-01 -7.402835e-02
## 32    7.719490e-02 -1.798166e-02
## 33    3.526119e-02  1.313665e-02
## 34    1.533302e-01 -9.026150e-02
## 35   -1.351227e-02  6.097042e-02
## 36    2.565872e-01 -1.603535e-01
## 37    3.265948e-02  2.532414e-02
## 38    1.859393e-01 -1.131938e-01
## 39    5.821899e-02 -4.616075e-03
## 40    9.206719e-02 -3.516168e-02
## 41    7.104558e-02 -1.027434e-02
## 42   -1.200248e-02  5.672862e-02
## 43    1.254094e-01 -6.697627e-02
## 44    7.834424e-02 -2.011233e-02
## 45   -6.388011e-03  5.945485e-02
## 46   -8.449605e-03  5.493368e-02
## 47   -1.756532e-02  6.124555e-02
## 48    1.791718e-02  3.411103e-02
## 49    2.002372e-01 -1.276925e-01
## 50    1.111673e-01 -5.170304e-02
## 51   -1.489558e-02  5.022479e-02
## 52    1.417431e-01 -6.204414e-02
## 53   -3.201112e-02  2.816372e-02
## 54   -1.657322e-02  6.361989e-02
## 55   -2.427544e-02  5.034578e-02
## 56   -7.962686e-03  5.620242e-02
## 57   -3.848377e-03  5.221126e-02
## 58    8.963594e-02 -2.375448e-02
## 59   -5.310054e-03  5.146378e-02
## 60    1.261109e-01 -6.881389e-02
## 61    1.032490e-01 -3.179489e-02
## 62    1.603468e-02  3.358742e-02
## 63    9.270250e-02 -2.965635e-02
## 64    1.754382e-01 -1.086251e-01
## 65   -1.197709e-02  5.353308e-02
## 66   -9.392811e-03  5.413174e-02
## 67    4.419810e-02  1.086372e-02
## 68    5.789006e-02  8.461919e-04
## 69   -4.517950e-03  5.060252e-02
## 70   -1.156088e-02  5.557617e-02
## 71    1.434314e-01 -8.434468e-02
## 72    2.225097e-01 -1.419800e-01
## 73    1.212896e-01 -6.346657e-02
## 74    1.544536e-01 -8.195176e-02
## 75   -2.242924e-02  6.176334e-02
## 76   -7.645760e-03  5.671142e-02
## 77   -2.619643e-02  4.504944e-02
## 78   -1.470034e-02  6.173715e-02
## 79    3.346767e-02  2.037501e-02
## 80   -6.331609e-03  5.391746e-02
## 81    7.969259e-02 -1.441402e-02
## 82    1.719404e-01 -1.117673e-01
## 83    1.716094e-01 -1.059410e-01
## 84    1.259966e-01 -6.279854e-02
## 85    3.427746e-02  1.385185e-02
## 86   -3.132612e-02  2.905351e-02
## 87    1.792946e-01 -1.118041e-01
## 88    1.384445e-01 -7.924413e-02
## 89    1.173732e-01 -6.057641e-02
## 90    2.098671e-01 -1.385296e-01
## 91    9.836321e-02 -2.625196e-02
## 92    1.456981e-01 -8.163663e-02
## 93   -2.388590e-03  5.106083e-02
## 94   -9.162665e-02 -4.343447e-02
## 95   -1.436152e-02  5.721592e-02
## 96    6.330944e-03  5.035400e-02
## 97   -3.772237e-04  4.930809e-02
## 98   -1.448849e-02  6.145782e-02
## 99    9.412741e-02 -3.255662e-02
## 100   2.348878e-02  3.380524e-02
## 101  -1.682248e-01 -1.613760e-01
## 102  -5.038055e-02  6.543911e-03
## 103  -3.822630e-02  3.727722e-02
## 104  -3.818462e-02  2.326085e-02
## 105  -1.714694e-02  5.290451e-02
## 106  -1.450527e-02  5.477573e-02
## 107   6.052131e-02 -1.239089e-03
## 108   4.124574e-02  1.022749e-02
## 109  -8.864281e-03  6.074169e-02
## 110  -2.291057e-02  5.392606e-02
## 111  -1.717481e-02  5.472069e-02
## 112   1.606656e-01 -8.010254e-02
## 113   8.480566e-03  4.001259e-02
## 114   1.944484e-01 -1.304739e-01
## 115   1.359756e-01 -8.038088e-02
## 116  -2.794411e-03  4.818199e-02
## 117  -2.855556e-02  4.979266e-02
## 118   2.326649e-01 -1.515322e-01
## 119   4.020151e-02  1.725374e-02
## 120  -4.095594e-03  5.459501e-02
## 121  -2.335039e-02  5.705361e-02
## 122  -2.701214e-02  5.701717e-02
## 123   3.779956e-03  4.756036e-02
## 124   3.240532e-02  2.643070e-02
## 125   1.148842e-01 -4.864390e-02
## 126   2.140490e-01 -1.329138e-01
## 127  -4.750548e-02  1.990487e-02
## 128   1.591957e-02  3.713941e-02
## 129  -5.417961e-02  5.972931e-03
## 130   1.893454e-01 -1.208273e-01
## 131   1.076677e-02  3.572795e-02
## 132   6.524999e-02 -7.449201e-03
## 133   1.789446e-01 -1.098747e-01
## 134  -1.554388e-02  4.951721e-02
## 135   7.869998e-02 -2.302992e-02
## 136   1.820972e-02  3.742061e-02
## 137  -3.014037e-02  4.406741e-02
## 138   5.666270e-02  3.987693e-04
## 139  -3.861332e-03  5.134750e-02
## 140   1.948995e-01 -1.168937e-01
## 141  -8.275575e-03  5.786551e-02
## 142  -5.961817e-02 -2.065383e-03
## 143  -2.789941e-02  3.662880e-02
## 144   2.522919e-01 -1.643998e-01
## 145  -2.912749e-02  4.984053e-02
## 146   1.331328e-01 -6.490533e-02
## 147  -5.766472e-02  9.898038e-05
## 148   2.270401e-01 -1.543725e-01
## 149   1.406000e-01 -7.031191e-02
## 150   1.177765e-03  4.663514e-02
## 151   7.407913e-02 -3.781878e-03
## 152   4.320676e-02  1.425749e-02
## 153   1.616415e-01 -9.246570e-02
## 154   2.478245e-02  2.270204e-02
## 155   1.603516e-02  3.759506e-02
## 156  -1.749424e-02  5.591669e-02
## 157  -1.696614e-01 -1.688548e-01
## 158   1.732936e-01 -8.956009e-02
## 159   5.748199e-02  5.168726e-03
## 160   3.448700e-02  2.219843e-02
## 161  -1.594862e-02  6.528311e-02
## 162  -1.095712e-02  5.672350e-02
## 163   2.205549e-01 -1.436061e-01
## 164   1.781563e-01 -1.125434e-01
## 165   1.708020e-01 -1.017284e-01
## 166   1.483089e-01 -7.485702e-02
## 167   2.055445e-01 -1.369751e-01
## 168   4.830596e-03  6.058401e-02
## 169  -1.311771e-02  6.021134e-02
## 170  -7.349340e-03  5.287209e-02
## 171  -7.250426e-02  1.172512e-02
## 172  -3.796359e-02  3.505913e-02
## 173  -2.464370e-02  6.425423e-02
## 174   1.311193e-03  4.247815e-02
## 175   4.983124e-03  4.579587e-02
## 176   1.126007e-01 -3.454514e-02
## 177  -7.029860e-03  5.532362e-02
## 178  -8.928012e-02 -3.818729e-02
## 179  -4.194381e-03  5.026452e-02
## 180  -4.029303e-02  2.762719e-02
## 181  -1.347101e-01 -1.250248e-01
## 182   1.255523e-01 -4.732190e-02
## 183  -4.281749e-02  4.115043e-02
## 184   9.501477e-03  4.488109e-02
## 185   1.026864e-01 -4.065665e-02
## 186   2.484652e-01 -1.653155e-01
## 187  -4.597018e-02  4.006510e-02
## 188   2.108282e-01 -1.361977e-01
## 189   1.140655e-01 -3.733630e-02
## 190  -2.687945e-02  5.693690e-02
## 191  -2.274249e-01 -2.497700e-01
## 192   9.444933e-02 -2.201842e-02
## 193   1.880605e-01 -1.114722e-01
## 194   3.068324e-01 -2.179311e-01
## 195   3.759089e-03  4.506104e-02
## 196   1.852368e-01 -1.118038e-01
## 197   1.785938e-01 -8.378517e-02
## 198  -1.852568e-02  6.117138e-02
## 199  -4.199954e-02  2.956702e-02
## 200   3.225733e-02  2.734452e-02
## 201  -2.353318e-02  5.584673e-02
## 202   3.087148e-01 -2.045081e-01
## 203  -5.832878e-04  5.211101e-02
## 204  -4.726584e-02  1.372795e-02
## 205   3.757288e-02  1.596733e-02
## 206  -4.929729e-02  2.420522e-02
## 207  -8.062911e-03  6.161852e-02
## 208  -2.534160e-02  4.606999e-02
## 209  -1.794765e-03  5.101818e-02
## 210  -2.583591e-02  6.554340e-02
## 211   3.453109e-01 -2.076417e-01
## 212   2.125641e-01 -1.269798e-01
## 213   3.568134e-02  2.193818e-02
## 214  -8.651938e-02 -1.974113e-03
## 215  -2.137310e-02  6.317039e-02
## 216  -7.500928e-02 -5.852401e-03
## 217  -1.367062e-02  5.969489e-02
## 218  -6.421348e-02  2.514243e-02
## 219  -1.141488e-02  5.532913e-02
## 220  -1.073934e-02  6.828789e-02
## 221  -3.655626e-02  2.516096e-02
## 222   4.255651e-03  1.103040e-01
## 223   6.992746e-02 -1.065372e-02
## 224   2.674859e-01 -1.536344e-01
## 225  -1.051375e-01 -5.830596e-02
## 226  -4.778660e-02  1.823237e-02
## 227  -8.032220e-03  6.715555e-02
## 228  -2.429622e-01 -2.561370e-01
## 229  -1.501056e-02  5.274867e-02
## 230  -3.078835e-03  5.625014e-02
## 231   5.563134e-02  2.213340e-02
## 232   2.196595e-01 -1.459483e-01
## 233   1.637239e-01 -9.439615e-02
## 234   1.843375e-01 -1.126528e-01
## 235  -9.420186e-02 -1.306865e-02
## 236  -5.775029e-03  5.491021e-02
## 237   7.752285e-02  6.091935e-03
## 238   1.752235e-02  3.294471e-02
## 239   1.580156e-01 -9.371369e-02
## 240  -4.233520e-02  3.830800e-02
## 241  -1.369527e-02  7.088555e-02
## 242   2.535107e-01 -1.753309e-01
## 243  -6.262942e-02  6.073904e-03
## 244  -1.094196e-02  5.953711e-02
## 245   9.920691e-02 -3.967465e-02
## 246  -1.190497e-02  6.557064e-02
## 247  -1.875604e-02  4.097765e-02
## 248   2.416454e-02  3.786539e-02
## 249  -1.841400e-02  4.710936e-02
## 250  -8.112540e-03  4.918771e-02
## 251   1.468977e-01 -5.795909e-02
## 252   2.524524e-01 -1.639287e-01
## 253   4.827698e-03  3.966848e-02
## 254   1.279840e-01 -5.593611e-02
## 255   2.432881e-01 -1.363776e-01
## 256   1.945936e-01 -1.270589e-01
## 257  -4.301026e-02  3.750269e-02
## 258  -1.269596e-01 -1.113467e-01
## 259   7.557430e-02  2.074866e-03
## 260   1.981512e-01 -8.709683e-02
## 261   1.924262e-01 -7.766119e-02
## 262  -2.151978e-02  6.231071e-02
## 263  -1.299244e-02  6.016126e-02
## 264  -5.223979e-02  2.224897e-02
## 265  -3.058521e-02  5.919772e-02
## 266  -5.731012e-02  2.508073e-02
## 267  -1.011678e-02  5.123702e-02
## 268  -1.318484e-02  5.964055e-02
## 269   1.438177e-02  5.364933e-02
## 270  -3.557970e-02  4.986698e-02
## 271  -7.476876e-03  7.102872e-02
## 272   3.621092e-02  4.211682e-02
## 273   1.194901e-01 -1.929698e-02
## 274   1.212398e-02  5.561154e-02
## 275   5.465181e-05  5.271388e-02
## 276   2.928456e-01 -1.958428e-01
## 277  -1.045578e-02  5.340208e-02
## 278   1.939551e-01 -1.188892e-01
## 279   1.569321e-02  4.635253e-02
## 280   2.355345e-01 -1.580152e-01
## 281  -3.217952e-02  5.776851e-02
## 282  -1.431473e-02  5.287951e-02
## 283  -1.411386e-02  5.263872e-02
## 284  -2.178236e-01 -2.289365e-01
## 285  -2.771311e-02  5.682231e-02
## 286   5.376737e-02  8.325783e-03
## 287   9.405953e-02 -1.342001e-02
## 288   9.079456e-02 -2.855762e-02
## 289   1.659253e-01 -9.137059e-02
## 290   1.825062e-01 -7.002661e-02
## 291  -4.713425e-02  1.776912e-02
## 292   2.840423e-03  4.544586e-02
## 293  -1.217248e-01 -9.546562e-02
## 294   1.090629e-01 -1.444886e-02
## 295  -2.282453e-02  5.958478e-02
## 296   1.037721e-02  4.016736e-02
## 297  -2.083833e-02  4.721765e-02
## 298  -1.920985e-01 -1.903758e-01
## 299  -4.379734e-02  1.927724e-02
## 300   1.250426e-01 -4.863867e-02
## 301  -2.347693e-01 -2.606742e-01
## 302   4.193614e-02  1.463623e-02
## 303   2.531368e-01 -1.346293e-01
## 304  -4.507905e-02  3.998901e-02
## 305   8.627133e-02 -2.180921e-02
## 306   2.174067e-01 -1.398206e-01
## 307   4.650361e-02  1.186580e-02
## 308  -3.393935e-02  3.356736e-02
## 309   1.875625e-02  4.318359e-02
## 310  -2.732554e-02  5.974651e-02
## 311  -2.470478e-02  3.673446e-02
## 312  -2.116183e-01 -2.355517e-01
## 313  -5.845212e-02  8.846095e-03
## 314  -2.316570e-03  5.413658e-02
## 315   8.136065e-03  5.243245e-02
## 316   2.833375e-01 -1.945721e-01
## 317  -4.433601e-02  3.233901e-02
## 318  -6.868096e-02  7.713853e-04
## 319   1.313133e-01 -4.497093e-02
## 320   9.399628e-02 -2.014672e-02
## 321  -1.275747e-02  6.347306e-02
## 322   4.783090e-02  2.547971e-02
## 323   9.806875e-02 -2.466264e-02
## 324  -8.082196e-02 -2.822082e-02
## 325  -2.599860e-02  3.503609e-02
## 326  -4.975711e-02  1.600593e-02
## 327  -6.407240e-02  1.059901e-02
## 328   1.925973e-02  4.300269e-02
## 329  -1.238815e-01 -5.664149e-02
## 330  -1.474469e-01 -1.126458e-01
## 331  -5.805058e-02  1.795160e-02
## 332   3.378696e-03  5.761325e-02
## 333  -4.918111e-02  1.470147e-02
## 334  -6.509983e-02  1.008147e-02
## 335  -5.715258e-02  3.488553e-02
## 336  -1.794339e-02  5.955280e-02
## 337  -8.757508e-02 -1.534877e-02
## 338  -6.910792e-02 -7.372562e-03
## 339  -1.359645e-02  5.597029e-02
## 340   3.870836e-02  8.529625e-03
## 341  -2.284167e-02  4.868702e-02
## 342   6.832414e-03  6.445569e-02
## 343  -1.641635e-01 -1.495660e-01
## 344  -4.890821e-02  2.849517e-02
## 345  -6.502442e-02  1.844248e-02
## 346  -1.109337e-01 -5.329416e-02
## 347   3.265443e-01 -2.123688e-01
## 348   2.005233e-01 -1.129196e-01
## 349  -5.772534e-02  1.487098e-02
## 350  -7.486693e-02 -1.297469e-02
## 351   2.052718e-01 -9.669510e-02
## 352  -2.198824e-02  5.681241e-02
## 353  -6.625879e-02  3.757335e-03
## 354  -2.614818e-02  4.664469e-02
## 355  -2.281207e-01 -2.317990e-01
## 356  -6.498578e-02 -1.368477e-02
## 357  -1.059893e-02  5.676231e-02
## 358   7.118978e-02 -1.230272e-02
## 359   2.111428e-01 -1.321994e-01
## 360   2.112238e-01 -1.375501e-01
## 361   1.577271e-01 -8.251644e-02
## 362   6.395930e-03  3.571813e-02
## 363   1.490211e-01 -7.821797e-02
## 364   1.880844e-04  5.403276e-02
## 365  -4.959190e-02  1.301331e-02
## 366  -1.758956e-01 -1.503450e-01
## 367   2.220960e-01 -1.433761e-01
## 368   8.358974e-03  4.053739e-02
## 369   2.993474e-01 -1.785889e-01
## 370  -2.835235e-03  5.211341e-02
## 371  -1.100700e-01 -4.002480e-02
## 372   1.617579e-02  4.225643e-02
## 373  -2.458887e-03  6.745826e-02
## 374  -7.693497e-02  2.079851e-03
## 375  -2.188689e-02  4.869010e-02
## 376  -7.441643e-03  6.924216e-02
## 377   9.014417e-03  5.078803e-02
## 378   1.292701e-02  5.804491e-02
## 379  -9.921995e-02 -3.039076e-02
## 380  -3.055363e-02  5.497330e-02
## 381  -2.941503e-02  5.586147e-02
## 382   5.333950e-02  5.027681e-03
## 383  -4.338370e-03  6.751187e-02
## 384  -9.595889e-02 -4.829840e-02
## 385  -8.907430e-02 -9.421857e-03
## 386   1.405588e-02  5.172413e-02
## 387  -8.775737e-04  5.858058e-02
## 388  -2.944385e-02  4.909149e-02
## 389  -2.643113e-01 -2.946973e-01
## 390   3.371056e-02  2.797251e-02
## 391   1.199030e-01 -5.276945e-02
## 392  -1.857949e-03  6.885969e-02
## 393  -2.151931e-02  6.316995e-02
## 394  -1.083102e-01 -7.081231e-02
## 395  -5.924818e-02  1.402582e-02
## 396   3.521830e-03  4.398835e-02
## 397   3.381798e-01 -2.264415e-01
## 398   1.783424e-01 -9.777534e-02
## 399   1.503219e-01 -5.934242e-02
## 400   1.468148e-03  5.366435e-02
## 401  -4.143163e-03  5.435967e-02
## 402  -6.228237e-02  2.086331e-02
## 403  -4.409395e-04  5.031979e-02
## 404   2.731827e-01 -1.503458e-01
## 405  -1.113374e-02  6.754774e-02
## 406   7.117846e-02  1.234949e-02
## 407   4.008380e-02  3.574260e-02
## 408   5.948292e-02  3.612041e-02
## 409  -4.655242e-03  5.545378e-02
## 410  -1.339388e-02  6.709285e-02
## 411  -7.663916e-02  3.507623e-03
## 412  -2.295474e-02  5.798355e-02
## 413  -1.854993e-01 -1.455468e-01
## 414  -2.032720e-02  6.640875e-02
## 415  -2.064856e-01 -2.056076e-01
## 416   6.498549e-03  4.568062e-02
## 417  -1.232684e-02  7.158673e-02
## 418  -5.826975e-02  2.763815e-02
## 419  -1.339435e-01 -5.867017e-02
## 420  -2.038096e-01 -1.801790e-01
## 421  -4.821283e-02  4.978978e-02
## 422  -2.739708e-01 -3.043935e-01
## 423  -1.121164e-02  6.093217e-02
## 424  -3.631554e-02  6.124170e-02
## 425  -1.237739e-01 -4.405838e-02
## 426   5.019152e-02  1.395308e-02
## 427   3.556780e-02  4.289633e-02
## 428   1.383262e-01 -7.214949e-02
## 429  -4.912638e-02  3.144809e-02
## 430  -1.862457e-02  5.705811e-02
## 431  -8.694950e-04  6.492301e-02
## 432  -4.202404e-02  3.941717e-02
## 433   8.126847e-02  1.747341e-02
## 434   2.948552e-02  3.730801e-02
## 435  -1.962752e-02  6.324220e-02
## 436  -5.038701e-02  3.493306e-02
## 437   5.550951e-03  6.178204e-02
## 438  -1.787928e-01 -1.466358e-01
## 439   2.478955e-01 -1.652881e-01
## 440   4.044298e-02  3.021926e-02
## 441   1.878974e-02  2.972943e-02
## 442   1.448869e-01 -6.013508e-02
## 443   2.265778e-01 -1.492772e-01
## 444  -1.890417e-01 -1.886453e-01
## 445   1.012998e-01 -6.452521e-03
## 446   6.363552e-02 -9.105681e-03
## 447  -2.088168e-02  3.756512e-02
## 448   1.912765e-01 -9.853605e-02
## 449  -3.324019e-03  5.669403e-02
## 450  -3.097958e-02  4.988489e-02
## 451  -4.192229e-02  3.045876e-02
## 452  -7.332917e-02  1.328329e-02
## 453  -1.747529e-01 -1.571141e-01
## 454   3.246433e-02  3.909260e-02
## 455   2.547840e-03  6.724754e-02
## 456   2.514551e-01 -1.316197e-01
## 457  -2.547692e-02  5.694724e-02
## 458  -1.621480e-02  6.071335e-02
## 459  -2.383226e-03  6.228928e-02
## 460  -4.343595e-02  7.501204e-02
## 461  -4.192173e-03  5.043337e-02
## 462  -1.302723e-02  6.912889e-02
## 463  -7.355295e-03  6.586553e-02
## 464  -9.719581e-02 -3.517842e-02
## 465  -4.989917e-02  4.355975e-02
## 466  -1.311784e-02  7.611665e-02
## 467  -4.211838e-02  5.906986e-02
## 468  -3.761648e-02  5.324642e-02
## 469  -3.786025e-03  6.810196e-02
## 470   2.091227e-02  4.871075e-02
## 471   2.110979e-01 -8.013388e-02
## 472   2.295801e-01 -1.498694e-01
## 473  -5.723021e-03  6.787414e-02
## 474  -1.344834e-03  6.208478e-02
## 475  -2.371286e-01 -2.409896e-01
## 476  -3.109892e-02  6.483823e-02
## 477  -1.511216e-02  6.082808e-02
## 478  -7.503500e-02 -1.461289e-02
## 479  -3.182490e-02  3.520352e-02
## 480   1.611900e-02  3.222052e-02
## 481   9.647687e-03  5.311291e-02
## 482   3.356509e-01 -1.969625e-01
## 483  -3.459445e-02  3.178989e-02
## 484   2.567803e-01 -1.619860e-01
## 485   7.136553e-02 -1.594873e-03
## 486  -1.971970e-02  6.119531e-02
## 487   2.759772e-01 -1.759686e-01
## 488  -2.104213e-01 -1.905685e-01
## 489   1.803329e-01 -1.027743e-01
## 490  -9.651378e-02 -3.177700e-02
## 491  -2.611027e-02  4.050266e-02
## 492  -2.012823e-02  5.866612e-02
## 493  -6.722848e-04  6.873868e-02
## 494   1.490032e-02  5.866087e-02
## 495   6.310518e-02  2.943170e-03
## 496   3.221841e-01 -1.976611e-01
## 497  -4.797382e-02  4.567744e-02
## 498  -1.577294e-02  6.618439e-02
## 499   8.610481e-02 -1.596755e-02
## 500  -2.615878e-02  5.633458e-02
## 501  -2.278595e-01 -2.225055e-01
## 502  -1.304437e-02  5.516427e-02
## 503   2.506544e-02  3.415713e-02
## 504   1.709201e-01 -7.811418e-02
## 505  -5.594455e-02  1.887228e-02
## 506  -7.287755e-02 -1.403112e-02
## 507   6.351707e-02 -3.959846e-03
## 508  -2.456700e-02  6.208890e-02
## 509  -3.303039e-02  4.222273e-02
## 510  -3.998682e-02  4.680113e-02
## 511  -1.882437e-01 -1.802985e-01
## 512  -1.857283e-01 -1.505577e-01
## 513  -8.670419e-03  6.183774e-02
## 514   2.035485e-03  4.976497e-02
## 515  -1.127293e-02  6.694584e-02
## 516   5.997623e-03  4.089018e-02
## 517   1.667554e-01 -7.426320e-02
## 518  -5.771870e-02  3.604752e-03
## 519  -5.215784e-02  3.232767e-02
## 520  -1.796151e-01 -1.296535e-01
## 521  -4.277041e-02  3.954288e-02
## 522  -1.303417e-01 -7.101557e-02
## 523  -4.422488e-02  4.087233e-02
## 524  -2.589132e-01 -2.930763e-01
## 525  -1.553976e-01 -9.011977e-02
## 526  -3.979863e-02  4.122858e-02
## 527  -1.065572e-01 -5.204022e-02
## 528  -7.393038e-03  7.246679e-02
## 529  -1.247452e-01 -6.628256e-02
## 530  -1.493827e-01 -1.079521e-01
## 531  -1.750109e-02  7.356432e-02
## 532  -3.062527e-02  6.009602e-02
## 533  -1.944971e-02  6.120756e-02
## 534   2.324695e-01 -1.076715e-01
## 535  -5.692660e-02  5.882984e-02
## 536  -7.273963e-02  4.202082e-03
## 537  -8.873127e-03  6.177506e-02
## 538  -1.705835e-01 -1.132659e-01
## 539  -1.360029e-02  7.601679e-02
## 540  -3.316626e-03  5.859496e-02
## 541  -2.777383e-01 -3.207600e-01
## 542  -2.537759e-02  7.074786e-02
## 543  -1.356086e-02  6.571279e-02
## 544  -1.322373e-01 -6.052432e-02
## 545  -1.198041e-02  8.867629e-02
## 546   1.600002e-01 -9.402005e-02
## 547  -3.583690e-02  4.694508e-02
## 548  -3.879001e-02  3.521893e-02
## 549  -1.916232e-01 -1.535156e-01
## 550  -3.275440e-01 -3.836972e-01
## 551  -7.096652e-02  5.668877e-03
## 552  -3.397136e-02  4.686179e-02
## 553  -2.864156e-02  7.843255e-02
## 554  -1.263634e-02  6.579742e-02
## 555  -7.503538e-02  4.856119e-03
## 556  -8.279200e-02 -4.726449e-03
## 557  -6.999103e-02  1.568439e-02
## 558  -2.825803e-02  5.346471e-02
## 559  -1.245548e-01 -5.285975e-02
## 560   2.537447e-01 -1.549902e-01
## 561   1.653101e-01 -8.805383e-02
## 562   6.283491e-03  5.781724e-02
## 563   3.432697e-02  3.686458e-02
## 564  -8.401733e-02 -3.234942e-02
## 565  -1.123781e-01 -6.316478e-02
## 566  -2.547278e-02  2.768328e-02
## 567   2.789991e-01 -1.633658e-01
## 568  -1.880613e-02  5.287991e-02
## 569  -9.504764e-02 -4.904978e-02
## 570   7.382279e-02 -6.797751e-03
## 571  -2.727414e-02  6.192864e-02
## 572  -1.029613e-02  7.053122e-02
## 573  -4.098002e-03  6.267327e-02
## 574   2.782778e-01 -1.788120e-01
## 575  -3.683454e-02  3.194050e-02
## 576   3.974908e-01 -2.537673e-01
## 577  -6.368310e-02  1.317246e-02
## 578  -6.102428e-02  8.970739e-03
## 579  -4.955049e-02  2.469930e-02
## 580  -6.721401e-02 -2.908168e-03
## 581  -5.226154e-02  5.112959e-02
## 582   7.245747e-02 -3.812834e-03
## 583  -4.720405e-04  6.742700e-02
## 584  -1.295661e-02  4.890052e-02
## 585  -2.422751e-02  6.088475e-02
## 586  -4.187464e-02  3.231776e-02
## 587  -4.937376e-02  4.287283e-02
## 588  -1.619221e-01 -1.195769e-01
## 589  -9.856093e-02 -5.310208e-02
## 590   2.242983e-01 -9.139428e-02
## 591  -4.256989e-02  4.212833e-02
## 592  -2.142706e-02  6.546406e-02
## 593  -8.250843e-03  7.339523e-02
## 594  -5.119713e-02  4.499190e-02
## 595  -1.906042e-03  6.245323e-02
## 596  -1.194617e-02  7.798668e-02
## 597   8.152272e-04  4.963608e-02
## 598   2.992747e-01 -1.676762e-01
## 599  -1.175758e-03  5.958847e-02
## 600  -1.012835e-02  7.177354e-02
## 601  -2.253412e-02  6.536908e-02
## 602   2.187232e-01 -1.012399e-01
## 603  -1.270871e-01 -8.136213e-02
## 604   5.168840e-02  3.304986e-02
## 605   4.785444e-02  3.359981e-02
## 606   2.014076e-01 -8.672290e-02
## 607  -1.168151e-02  6.535399e-02
## 608   3.275930e-01 -2.166623e-01
## 609   1.699958e-03  5.476188e-02
## 610  -3.216914e-03  4.912311e-02
## 611   1.355257e-01 -1.736522e-02
## 612  -2.031408e-02  4.035051e-02
## 613   2.870803e-02  4.202504e-02
## 614   3.026170e-01 -1.493820e-01
## 615  -2.397424e-01 -2.195066e-01
## 616   1.183472e-01 -3.543322e-02
## 617   1.197836e-01 -4.276178e-02
## 618   2.089948e-01 -1.349787e-01
## 619   8.664931e-02 -1.672131e-02
## 620   3.095073e-02  5.113219e-02
## 621   1.112203e-01 -9.712918e-03
## 622   2.006581e-01 -1.287918e-01
## 623   1.661589e-01 -6.288671e-02
## 624   7.125984e-03  4.723647e-02
## 625  -5.011836e-02  3.037520e-02
## 626   1.542582e-01 -8.005909e-02
## 627   5.599532e-02  2.804584e-02
## 628   5.723554e-03  2.726308e-02
## 629   8.184986e-02 -1.892885e-02
## 630   1.745983e-01 -6.772938e-02
## 631   1.058683e-01 -4.133243e-02
## 632  -1.212594e-01 -7.525987e-02
## 633  -2.442483e-02  4.351427e-02
## 634  -6.902094e-02 -4.458613e-03
## 635   3.253319e-01 -1.930761e-01
## 636  -5.052513e-03  5.154238e-02
## 637   2.544177e-01 -1.240232e-01
## 638  -8.325123e-02 -1.132717e-02
## 639   1.663055e-01 -8.374859e-02
## 640  -1.341479e-01 -9.755118e-02
## 641  -1.908054e-01 -1.692017e-01
## 642   2.581579e-01 -1.406782e-01
## 643   5.242410e-02  9.256842e-03
## 644  -8.782505e-02 -1.978145e-02
## 645   2.651712e-01 -1.661302e-01
## 646  -7.797401e-02 -1.354168e-03
## 647  -2.782891e-01 -3.137884e-01
## 648  -2.091697e-01 -1.902940e-01
## 649  -2.117812e-02  6.795119e-02
## 650   2.878640e-02  4.798216e-02
## 651  -5.136336e-04  5.710244e-02
## 652  -1.924612e-01 -1.663548e-01
## 653  -1.597100e-02  7.225544e-02
## 654   4.905670e-02  1.889253e-02
## 655   1.524050e-02  3.227010e-02
## 656   8.089159e-03  6.144363e-02
## 657  -4.605767e-02  3.983170e-02
## 658  -8.274723e-02 -1.995614e-02
## 659  -6.908044e-02  7.574838e-03
## 660  -1.102757e-02  5.536327e-02
## 661  -5.592389e-02  4.390638e-02
## 662  -4.849121e-03  6.839295e-02
## 663   2.282682e-02  5.540138e-02
## 664  -9.144099e-02 -2.657381e-02
## 665   3.657081e-02  5.542018e-02
## 666  -4.414855e-02  4.768233e-02
## 667  -3.487506e-02  5.478832e-02
## 668  -3.405676e-02  5.359617e-02
## 669  -6.501313e-03  6.541780e-02
## 670  -1.543597e-02  5.871809e-02
## 671  -3.258544e-01 -3.743695e-01
## 672  -1.459695e-02  6.737106e-02
## 673   1.322124e-01 -3.952096e-02
## 674  -1.268100e-01 -4.081849e-02
## 675  -3.720022e-03  6.283370e-02
## 676  -4.094943e-02  4.608205e-02
## 677  -3.738032e-02  3.991197e-02
## 678  -2.039904e-02  7.222896e-02
## 679  -5.246208e-02  2.792492e-02
## 680  -1.468625e-02  6.835356e-02
## 681  -1.412477e-02  8.272724e-02
## 682  -4.000871e-03  6.523995e-02
## 683  -3.704312e-02  5.331109e-02
## 684  -7.651672e-03  7.148463e-02
## 685  -2.063207e-02  5.114010e-02
## 686  -7.670479e-02 -1.102318e-02
## 687  -2.503362e-01 -2.757023e-01
## 688  -3.344975e-02  5.455261e-02
## 689  -3.639955e-02  5.252024e-02
## 690  -7.138924e-02 -2.542230e-03
## 691  -1.401550e-01 -8.935872e-02
## 692   1.429138e-01 -3.274183e-02
## 693   1.946572e-01 -1.124886e-01
## 694   6.204902e-02  7.653629e-03
## 695   2.548584e-02  5.724750e-02
## 696   2.246799e-01 -1.099199e-01
## 697   3.791672e-03  5.088946e-02
## 698  -1.167276e-02  6.852321e-02
## 699  -4.870308e-02  1.346921e-02
## 700   9.443995e-02  1.311691e-02
## 701  -3.538110e-02  4.170210e-02
## 702  -1.539819e-01 -1.001333e-01
## 703  -2.085339e-02  6.063683e-02
## 704  -6.665910e-03  5.428103e-02
## 705   2.059526e-02  7.440304e-02
## 706   4.768613e-03  5.562972e-02
## 707  -7.712379e-02 -1.860574e-02
## 708  -3.163371e-02  4.911094e-02
## 709  -2.701644e-01 -3.160131e-01
## 710   5.123284e-03  6.628928e-02
## 711  -3.867928e-02  5.813109e-02
## 712   3.699848e-02 -7.519389e-03
## 713  -1.306356e-02  6.503484e-02
## 714  -1.018489e-01 -6.070219e-02
## 715   5.538256e-02  7.132867e-03
## 716  -5.516324e-02  1.026531e-02
## 717  -3.506456e-02  5.491775e-02
## 718  -1.297024e-01 -9.475565e-02
## 719  -8.503573e-04  7.499888e-02
## 720   7.550788e-02 -5.970173e-03
## 721  -1.573863e-01 -1.076798e-01
## 722   2.833500e-01 -1.588385e-01
## 723  -2.073450e-02  5.746346e-02
## 724   3.724265e-02  1.717457e-02
## 725  -1.559700e-02  5.426616e-02
## 726   6.515258e-03  4.393728e-02
## 727   3.231038e-01 -1.941847e-01
## 728  -3.222202e-02  5.212911e-02
## 729  -2.260951e-02  5.552260e-02
## 730  -2.013397e-01 -2.008024e-01
## 731   7.593035e-03  4.270015e-02
## 732  -2.941189e-01 -3.452033e-01
## 733  -1.772762e-02  6.192638e-02
## 734  -6.197727e-02  1.343343e-02
## 735  -7.851045e-02 -1.174847e-03
## 736  -3.685368e-02  5.997616e-02
## 737   5.071212e-02  3.791194e-02
## 738  -7.585685e-02  4.451355e-03
## 739  -1.225456e-01 -7.735801e-02
## 740   1.024202e-02  5.322106e-02
## 741   7.107484e-02  2.388912e-02
## 742   1.249726e-01 -9.517234e-03
## 743  -3.085993e-03  6.610806e-02
## 744  -9.441787e-02 -9.689354e-03
## 745  -7.152529e-02  1.733384e-02
## 746  -3.381598e-01 -4.163188e-01
## 747  -3.742974e-02  2.366157e-02
## 748   1.037173e-01 -2.729399e-02
## 749  -1.753666e-01 -1.380643e-01
## 750  -9.649384e-03  7.005899e-02
## 751   1.851993e-03  7.031835e-02
## 752   1.040021e-02  4.985463e-02
## 753  -1.358458e-01 -5.486691e-02
## 754  -1.947559e-01 -1.546160e-01
## 755  -7.103109e-03  7.663696e-02
## 756  -1.158760e-01 -5.465018e-02
## 757   6.695969e-02  1.674592e-02
## 758  -6.552904e-02  1.965171e-02
## 759   8.667213e-02  2.604772e-02
## 760  -3.266133e-02  3.326067e-02
## 761  -5.728846e-04  7.757389e-02
## 762  -1.220718e-02  5.830169e-02
## 763   6.385620e-03  7.230568e-02
## 764  -7.761067e-02 -2.821325e-02
## 765   1.588225e-02  9.112067e-02
## 766  -4.488941e-02  4.675503e-02
## 767  -7.604398e-03  4.505824e-02
## 768  -9.586648e-02 -3.638259e-02
## 769  -2.299793e-02  5.679228e-02
## 770  -1.191978e-02  5.522169e-02
## 771   2.120782e-03  9.575933e-02
## 772  -1.634133e-01 -1.228532e-01
## 773   3.531282e-02  5.588003e-02
## 774  -1.331351e-02  5.913915e-02
## 775  -9.035010e-02 -3.237063e-02
## 776   6.147522e-02  1.098237e-02
## 777  -2.721934e-01 -2.708623e-01
## 778  -1.828540e-01 -1.443701e-01
## 779  -7.459240e-02 -2.542263e-02
## 780   1.376835e-02  3.540743e-02
## 781  -2.592453e-01 -2.570413e-01
## 782   2.073217e-01 -1.186243e-01
## 783  -2.728948e-02  5.194049e-02
## 784  -2.142296e-01 -2.222164e-01
## 785   2.329533e-01 -1.454152e-01
## 786  -5.172671e-02  2.165080e-02
## 787   1.374259e-01 -5.860567e-02
## 788   1.656776e-01 -8.985355e-02
## 789   1.352718e-01 -4.941578e-02
## 790   5.225241e-02  5.464905e-02
## 791   1.652357e-02  5.945236e-02
## 792  -1.689116e-02  6.301816e-02
## 793   2.597657e-01 -1.578679e-01
## 794   1.532719e-02  5.204106e-02
## 795  -2.194533e-02  5.815430e-02
## 796   2.524155e-02  6.284369e-02
## 797   1.182900e-01 -2.039008e-04
## 798  -3.431044e-02  4.189735e-02
## 799   1.058571e-02  5.804474e-02
## 800   8.845634e-02  2.805763e-02
## 801   2.658551e-01 -1.646435e-01
## 802   7.711096e-03  5.272185e-02
## 803   3.307072e-02  5.389736e-02
## 804  -3.724693e-02  2.316261e-02
## 805   4.558461e-02  2.081445e-02
## 806  -4.331448e-02  3.953069e-02
## 807  -3.709559e-02  6.933490e-02
## 808   1.003553e-02  5.101369e-02
## 809  -5.658377e-02  3.865939e-02
## 810  -1.102286e-01 -3.918822e-02
## 811  -1.052315e-01 -3.506377e-02
## 812   4.115092e-02  3.923433e-02
## 813   8.758648e-02  2.602323e-03
## 814   1.491996e-01 -5.723103e-02
## 815   2.947611e-01 -1.877711e-01
## 816  -4.158480e-02  4.383590e-02
## 817  -2.392471e-01 -2.699087e-01
## 818   3.521232e-02  2.338689e-02
## 819  -6.151175e-02  2.070008e-03
## 820  -4.142899e-02  6.272887e-02
## 821  -3.874241e-02  5.342947e-02
## 822  -2.203084e-01 -2.056800e-01
## 823  -9.722567e-03  8.276426e-02
## 824   4.776227e-03  1.036841e-01
## 825  -4.598277e-02  6.932429e-02
## 826   3.505452e-03  9.601369e-02
## 827  -1.952590e-02  5.896858e-02
## 828  -3.314271e-02  6.853045e-02
## 829  -3.222059e-02  6.629073e-02
## 830   7.683691e-02 -6.920049e-03
## 831  -5.257799e-02  3.006359e-02
## 832   7.622139e-03  2.195666e-02
## 833   1.361135e-02  1.025604e-01
## 834  -5.262835e-02  6.715481e-02
## 835  -2.827974e-01 -3.108700e-01
## 836   8.805424e-02  3.814801e-02
## 837  -7.235525e-02  8.670290e-03
## 838  -4.743147e-02  4.804073e-02
## 839   1.548625e-02  3.875953e-02
## 840  -1.374425e-01 -1.021777e-01
## 841  -2.031042e-02  6.281629e-02
## 842  -1.277335e-01 -5.660706e-02
## 843  -4.938848e-02  5.312234e-02
## 844  -2.320164e-01 -2.511325e-01
## 845  -1.367830e-01 -7.863309e-02
## 846  -1.902049e-01 -1.604746e-01
## 847   7.087994e-03  7.479351e-02
## 848  -7.815142e-02  2.353817e-02
## 849   2.905330e-02  9.160509e-02
## 850  -1.741053e-02  6.322991e-02
## 851  -6.513498e-03  7.056380e-02
## 852  -2.916866e-02  6.688109e-02
## 853   1.049327e-02  6.634820e-02
## 854  -1.283188e-01 -7.998532e-02
## 855  -4.856432e-02  4.830619e-02
## 856   8.442216e-02  1.648236e-02
## 857  -1.049465e-01 -5.258794e-02
## 858  -2.297840e-01 -2.392015e-01
## 859  -1.259987e-01 -9.276169e-02
## 860   2.548257e-01 -1.654738e-01
## 861  -4.789846e-02  4.203447e-02
## 862  -3.367639e-02  5.554861e-02
## 863  -1.341037e-01 -7.014308e-02
## 864  -1.491742e-01 -8.635388e-02
## 865  -2.796855e-01 -3.247739e-01
## 866  -1.093997e-01 -6.756825e-02
## 867  -3.417684e-02  6.546217e-02
## 868  -3.532524e-02  5.303459e-02
## 869  -2.240878e-01 -2.166253e-01
## 870  -4.468105e-02  3.196122e-02
## 871  -1.887154e-02  6.749413e-02
## 872  -1.251750e-01 -7.772481e-02
## 873   2.513320e-01 -1.164616e-01
## 874  -6.471121e-03  7.130973e-02
## 875  -4.516499e-02  1.451306e-02
## 876  -2.222890e-02  6.157486e-02
## 877   3.546170e-02  3.888987e-02
## 878   1.873712e-02  7.026244e-02
## 879   1.391222e-01 -7.099320e-02
## 880  -3.039815e-02  6.042761e-02
## 881  -4.439095e-02  2.832194e-02
## 882  -1.091744e-02  6.431859e-02
## 883  -9.671449e-02 -3.050935e-02
## 884  -2.251013e-02  3.424576e-02
## 885  -1.856935e-01 -1.638045e-01
## 886  -6.193076e-02  2.054806e-02
## 887  -1.277818e-02  8.082290e-02
## 888   6.003070e-03  8.447041e-02
## 889  -2.662283e-02  6.872677e-02
## 890  -1.229314e-01 -7.069590e-02
## 891  -1.819198e-01 -1.812328e-01
## 892  -4.947667e-02  5.747136e-02
## 893  -7.694808e-02  6.717714e-03
## 894   6.610377e-02  8.802920e-03
## 895  -1.815350e-02  7.248298e-02
## 896  -5.234964e-02  3.541776e-02
## 897   4.967526e-02  8.862327e-02
## 898  -1.497741e-02  7.598350e-02
## 899  -5.343959e-02  1.452499e-02
## 900  -2.552475e-02  5.760593e-02
## 901   1.085310e-01  1.346078e-02
## 902   8.599814e-02  1.300573e-02
## 903  -3.448124e-02  4.951497e-02
## 904  -1.857835e-01 -1.602557e-01
## 905   2.173577e-01 -1.097419e-01
## 906  -2.151370e-02  7.007776e-02
## 907   1.446233e-01 -7.759116e-02
## 908  -1.356107e-01 -8.369300e-02
## 909  -4.053561e-02  5.001684e-02
## 910  -3.535074e-02  3.485134e-02
## 911  -2.408563e-02  5.215706e-02
## 912  -2.227962e-02  6.241564e-02
## 913  -3.877777e-02  5.179726e-02
## 914  -3.620246e-02  5.010552e-02
## 915  -1.546196e-02  7.004723e-02
## 916   2.197757e-01 -9.435701e-02
## 917  -2.085896e-01 -2.141194e-01
## 918  -2.739147e-01 -3.165294e-01
## 919  -3.591382e-01 -4.409193e-01
## 920  -2.687473e-02  5.761396e-02
## 921   4.847939e-02  1.144430e-02
## 922  -4.343885e-02  3.692625e-02
## 923  -1.553461e-02  4.331773e-02
## 924  -8.673614e-02 -2.441163e-02
## 925   1.590634e-01 -8.403889e-02
## 926   2.600370e-01 -1.541100e-01
## 927   1.956773e-02  5.502198e-02
## 928  -2.607423e-02  4.492755e-02
## 929   2.527239e-03  4.746567e-02
## 930  -2.297839e-01 -2.378770e-01
## 931   1.458334e-01 -4.244930e-03
## 932   4.350959e-01 -2.662268e-01
## 933  -1.364163e-01 -8.615125e-02
## 934  -1.174459e-01 -8.316907e-02
## 935  -4.817793e-02  2.828208e-02
## 936   1.100938e-01 -1.779870e-02
## 937  -2.535461e-02  6.015455e-02
## 938  -1.250174e-02  2.979055e-02
## 939  -6.238164e-03  8.827242e-02
## 940  -6.484149e-02  1.199817e-02
## 941  -3.304892e-02  6.109983e-02
## 942   1.719494e-01 -9.324931e-02
## 943  -8.242696e-02 -2.855871e-02
## 944   2.611076e-02  8.086611e-02
## 945  -2.432009e-02  5.080168e-02
## 946  -1.292153e-01 -1.076249e-01
## 947  -2.850135e-01 -3.054444e-01
## 948   2.156908e-01 -7.438451e-02
## 949   1.549817e-01 -6.385349e-02
## 950   1.704985e-02  4.304129e-02
## 951  -3.767143e-02  4.808230e-02
## 952   1.775280e-03  7.087226e-02
## 953   1.040398e-01 -3.293556e-02
## 954  -3.075435e-02  5.531631e-02
## 955  -2.897828e-01 -3.313316e-01
## 956  -1.004222e-01 -3.173483e-02
## 957   4.023059e-01 -2.375857e-01
## 958  -2.333779e-02  6.024326e-02
## 959  -3.839612e-02  6.118120e-02
## 960  -3.009363e-02  4.926863e-02
## 961  -2.898483e-02  6.585916e-02
## 962   9.979914e-03  1.072569e-01
## 963  -1.842813e-01 -1.772843e-01
## 964  -1.085048e-02  9.050040e-02
## 965   3.233341e-02  4.633979e-02
## 966  -5.002954e-05  8.061813e-02
## 967  -6.436037e-02  1.867376e-02
## 968  -1.760767e-02  6.008719e-02
## 969  -2.265460e-01 -2.014816e-01
## 970  -1.147538e-02  6.835715e-02
## 971  -1.608394e-01 -1.259926e-01
## 972  -9.729412e-03  8.467396e-02
## 973  -2.900059e-02  6.387464e-02
## 974  -2.106527e-01 -1.979354e-01
## 975  -2.816273e-01 -3.356352e-01
## 976  -1.303570e-02  6.898134e-02
## 977  -1.813535e-02  6.831120e-02
## 978  -1.633610e-02  7.125392e-02
## 979  -1.862877e-01 -1.588670e-01
## 980  -3.211394e-02  5.968494e-02
## 981   1.625782e-01 -3.150443e-02
## 982  -2.554634e-02  6.419050e-02
## 983  -2.851908e-02  5.696256e-02
## 984  -2.493248e-01 -2.613123e-01
## 985   6.706510e-03  7.623015e-02
## 986  -1.136848e-01 -4.889631e-02
## 987   5.199980e-03  5.184192e-02
## 988  -1.545490e-02  6.579352e-02
## 989  -1.013044e-02  8.404428e-02
## 990  -8.921543e-03  8.716670e-02
## 991   2.093700e-01 -1.122294e-01
## 992  -2.737907e-01 -3.171692e-01
## 993   2.122284e-03  1.104905e-01
## 994  -5.191389e-02  2.753578e-02
## 995  -1.784972e-02  6.025583e-02
## 996  -2.230220e-02  6.119115e-02
## 997  -1.557392e-01 -1.297046e-01
## 998  -8.213021e-03  8.051633e-02
## 999   1.561613e-01 -2.953844e-02
## 1000 -4.669928e-02  3.910775e-02
## 1001 -8.352907e-02  2.034445e-02
## 1002 -3.575454e-02  5.554454e-02
## 1003  6.526820e-03  6.056585e-02
## 1004 -1.371849e-01 -7.453486e-02
## 1005  1.135276e-01  1.323269e-02
## 1006 -1.728786e-03  1.205880e-01
## 1007 -3.904226e-02  4.455216e-02
## 1008 -1.295570e-02  6.772342e-02
## 1009  2.198493e-01 -1.239551e-01
## 1010  2.878747e-01 -1.063412e-01
## 1011  3.596811e-03  1.049710e-01
## 1012 -2.804335e-01 -3.226019e-01
## 1013 -2.306481e-02  6.295833e-02
## 1014  2.508543e-01 -1.471166e-01
## 1015  2.127951e-02  8.166779e-02
## 1016 -1.729637e-01 -1.505284e-01
## 1017  2.699952e-01 -1.667393e-01
## 1018 -1.436147e-02  8.030131e-02
## 1019  5.569063e-02  1.795427e-02
## 1020 -2.010791e-02  6.154715e-02
## 1021 -2.279143e-01 -2.460651e-01
## 1022 -1.522410e-03  6.974192e-02
## 1023 -5.076192e-02  2.147993e-02
## 1024 -9.881355e-02 -5.670749e-02
## 1025 -5.942858e-02  2.839623e-02
## 1026 -1.037721e-01 -7.027047e-02
## 1027 -1.053372e-02  8.825259e-02
## 1028 -1.489396e-02  6.912786e-02
## 1029 -1.644367e-01 -1.332963e-01
## 1030  9.269392e-03  1.141126e-01
## 1031 -2.206940e-02  6.291047e-02
## 1032  2.156600e-02  1.031245e-01
## 1033  2.712862e-01 -1.533721e-01
## 1034 -2.979536e-02  6.623627e-02
## 1035  7.754701e-02  3.978316e-02
## 1036 -1.094418e-02  5.339560e-02
## 1037 -4.061183e-02  3.935748e-02
## 1038 -1.042574e-01 -4.454396e-02
## 1039  2.304924e-02  2.931326e-02
## 1040  2.690476e-02  3.500278e-02
## 1041  1.740431e-01 -7.734342e-02
## 1042  1.923170e-01 -1.089987e-01
## 1043  1.837335e-01 -7.514565e-02
## 1044  2.348003e-02  4.039642e-02
## 1045  1.187700e-02  3.339226e-02
## 1046  6.136488e-02 -5.612061e-03
## 1047 -7.203400e-02 -4.579515e-03
## 1048 -2.169708e-02  1.972683e-02
## 1049 -9.710088e-02 -6.024662e-02
## 1050  4.323494e-01 -2.670208e-01
## 1051  2.885362e-01 -1.823097e-01
## 1052 -1.888680e-01 -1.733587e-01
## 1053  4.183934e-01 -2.182076e-01
## 1054  4.613785e-01 -2.936909e-01
## 1055 -2.268120e-02  3.892912e-02
## 1056  4.721345e-01 -2.985095e-01
## 1057 -4.252527e-02  2.838007e-02
## 1058 -7.735377e-02  3.852388e-03
## 1059 -4.667278e-03  1.000174e-01
## 1060  1.689230e-01  7.554182e-03
## 1061  2.850379e-01 -1.534270e-01
## 1062  3.393624e-01 -2.067798e-01
## 1063 -3.081689e-01 -3.699025e-01
## 1064 -1.845047e-02  6.833968e-02
## 1065  2.825962e-01 -1.306539e-01
## 1066  6.463731e-03  5.605036e-02
## 1067 -1.111624e-01 -6.028377e-02
## 1068 -2.134866e-01 -2.256809e-01
## 1069 -6.008711e-02  6.132437e-03
## 1070  1.688310e-01 -8.290883e-02
## 1071 -1.319213e-01 -1.014011e-01
## 1072  2.851244e-01 -1.024253e-01
## 1073 -1.258056e-02  6.720833e-02
## 1074 -4.525480e-02  2.171532e-02
## 1075 -9.297750e-02 -5.386264e-02
## 1076  2.699489e-02  4.909956e-02
## 1077  1.979977e-01 -2.455552e-02
## 1078  3.777985e-01 -1.778969e-01
## 1079 -3.451299e-02  4.427437e-02
## 1080 -3.784427e-02  3.575328e-02
## 1081  2.559048e-01 -1.281116e-01
## 1082  1.042818e-01  1.921369e-02
## 1083  5.014637e-02  5.960232e-02
## 1084 -1.598135e-02  6.275148e-02
## 1085  1.786429e-01 -7.521012e-02
## 1086  4.310065e-03  4.999529e-02
## 1087 -2.131156e-02  4.178244e-02
## 1088 -3.121305e-02  4.132374e-02
## 1089 -6.172995e-02 -9.931120e-04
## 1090  9.398948e-02  6.634010e-03
## 1091 -9.422076e-02 -2.798755e-02
## 1092 -1.993565e-02  6.642159e-02
## 1093 -3.414501e-01 -4.112845e-01
## 1094 -3.382144e-03  6.122938e-02
## 1095 -1.396382e-01 -1.215793e-01
## 1096 -1.861166e-01 -1.912017e-01
## 1097 -3.062941e-02  5.265445e-02
## 1098 -8.996251e-03  7.872935e-02
## 1099 -8.027244e-02 -2.574263e-03
## 1100 -1.745722e-01 -1.355993e-01
## 1101 -3.174542e-02  4.899431e-02
## 1102 -1.233647e-02  7.800655e-02
## 1103 -6.515043e-02  2.655096e-02
## 1104 -8.714290e-02 -5.644739e-03
## 1105  9.450946e-02  4.312202e-02
## 1106 -7.098324e-02  4.033380e-02
## 1107  2.922386e-02  6.195956e-02
## 1108  3.047200e-03  7.758106e-02
## 1109 -3.376416e-03  7.807759e-02
## 1110 -2.480781e-01 -2.678423e-01
## 1111 -4.886412e-03  7.310577e-02
## 1112 -7.120361e-02 -7.362874e-03
## 1113  4.790035e-03  9.109094e-02
## 1114 -1.664619e-01 -1.105207e-01
## 1115 -6.606066e-03  7.974344e-02
## 1116 -4.602710e-02  4.519474e-02
## 1117 -3.414143e-02  4.599708e-02
## 1118 -1.475002e-02  5.721767e-02
## 1119 -3.982433e-02  5.995012e-02
## 1120 -1.951795e-01 -1.990773e-01
## 1121 -2.594545e-01 -2.995126e-01
## 1122 -1.851097e-02  7.518313e-02
## 1123  1.486173e-02  4.885072e-02
## 1124 -1.018474e-01 -4.557018e-02
## 1125  1.195434e-01  5.611387e-02
## 1126  2.731467e-01 -8.576091e-02
## 1127 -4.098205e-03  1.072815e-01
## 1128 -3.900698e-02  4.849246e-02
## 1129 -1.050829e-01 -5.199812e-02
## 1130 -1.436597e-02  6.046386e-02
## 1131 -1.251518e-02  7.300216e-02
## 1132 -1.495044e-02  5.332652e-02
## 1133  3.172597e-01 -1.583537e-01
## 1134  3.012128e-03  6.927388e-02
## 1135 -1.347994e-01 -1.173726e-01
## 1136 -3.500977e-01 -4.340907e-01
## 1137 -1.270530e-01 -1.052506e-01
## 1138 -8.575557e-03  6.534576e-02
## 1139 -2.158513e-02  6.359248e-02
## 1140  1.252726e-01 -4.679312e-02
## 1141  3.854028e-02  4.626395e-02
## 1142  1.899257e-01 -7.911962e-02
## 1143 -7.079141e-02  2.746211e-03
## 1144 -1.495650e-02  6.679734e-02
## 1145  2.312837e-02  3.279414e-02
## 1146  5.223359e-02 -3.998120e-02
## 1147  3.675234e-03  8.721696e-02
## 1148  1.164134e-01 -3.818362e-02
## 1149  2.717746e-01 -1.232529e-01
## 1150  3.624518e-02  2.058695e-02
## 1151 -4.507504e-02  4.649019e-02
## 1152 -8.844184e-03  6.653147e-02
## 1153 -9.785797e-02 -4.752937e-02
## 1154  1.712126e-01 -7.636376e-02
## 1155 -1.522793e-02  7.198272e-02
## 1156 -8.669371e-02 -4.253653e-02
## 1157 -3.520018e-02  5.080513e-02
## 1158 -1.073681e-01 -1.689831e-02
## 1159 -1.343501e-01 -1.148063e-01
## 1160  3.819191e-01 -1.845006e-01
## 1161  2.979159e-01 -1.811001e-01
## 1162 -4.243194e-02  2.335698e-02
## 1163 -5.380991e-02  3.927951e-02
## 1164 -2.853845e-01 -3.337145e-01
## 1165 -2.894959e-01 -3.333837e-01
## 1166 -1.232291e-01 -8.798127e-02
## 1167 -7.419391e-02  1.640581e-02
## 1168 -7.911442e-02 -1.163767e-02
## 1169 -6.747618e-04  9.271319e-02
## 1170  2.894309e-01 -1.789710e-01
## 1171 -2.422647e-02  5.681717e-02
## 1172 -7.393744e-05  9.136925e-02
## 1173 -8.841851e-03  8.041513e-02
## 1174 -3.583352e-02  3.843696e-02
## 1175 -9.761385e-03  7.051981e-02
## 1176  4.369003e-02  3.041432e-02
## 1177 -1.459116e-01 -1.335250e-01
## 1178  6.894508e-02  2.454258e-02
## 1179 -1.238340e-01 -5.843750e-02
## 1180  3.557927e-01 -2.004740e-01
## 1181 -6.977535e-04  1.000452e-01
## 1182 -1.780969e-02  6.037880e-02
## 1183 -6.283802e-02  2.140755e-02
## 1184 -2.816619e-02  5.390712e-02
## 1185 -3.772550e-02  4.144034e-02
## 1186 -1.552072e-02  5.908861e-02
## 1187 -1.395625e-01 -1.132427e-01
## 1188 -1.770925e-02  6.867670e-02
## 1189 -1.276286e-01 -7.148566e-02
## 1190 -1.925371e-02  6.419954e-02
## 1191 -9.348425e-03  7.493091e-02
## 1192 -7.538288e-02 -1.181142e-02
## 1193  1.787124e-01 -6.074372e-02
## 1194 -4.104128e-02  1.927326e-02
## 1195 -3.071678e-04  6.754736e-02
## 1196  8.408381e-02  5.723562e-02
## 1197  1.265271e-03  7.215541e-02
## 1198  8.219801e-03  9.799785e-02
## 1199 -3.346619e-03  5.109859e-02
## 1200 -4.531359e-02  1.898284e-02
## 1201 -4.771103e-02  6.908982e-02
## 1202  1.090193e-01  8.157981e-03
## 1203 -2.470823e-02  6.590555e-02
## 1204 -3.152410e-01 -3.650182e-01
## 1205 -9.177169e-03  8.990297e-02
## 1206  1.395927e-02  7.028627e-02
## 1207 -2.806586e-01 -3.003882e-01
## 1208 -5.846657e-02  2.054744e-02
## 1209 -1.473200e-02  8.170910e-02
## 1210 -2.394637e-01 -2.666954e-01
## 1211 -1.163348e-01 -6.538597e-02
## 1212 -2.311346e-01 -2.490598e-01
## 1213 -6.598367e-02  3.380196e-03
## 1214  1.712934e-01  1.312852e-02
## 1215  2.319517e-01 -7.678103e-02
## 1216  2.801194e-01 -1.887112e-01
## 1217 -1.953198e-02  9.283101e-02
## 1218  3.822702e-03  8.423755e-02
## 1219  4.715565e-01 -2.613937e-01
## 1220  2.159505e-01 -8.907447e-02
## 1221 -3.754650e-03  1.014966e-01
## 1222 -2.018130e-01 -1.899077e-01
## 1223 -2.396155e-02  6.717587e-02
## 1224 -2.619773e-01 -2.931083e-01
## 1225 -7.644630e-02 -2.564339e-02
## 1226  3.174623e-01 -1.337912e-01
## 1227  4.249230e-01 -2.482647e-01
## 1228 -6.598950e-02  1.259154e-04
## 1229  1.552107e-01 -4.743986e-02
## 1230  1.871355e-01 -2.618049e-02
## 1231 -1.518435e-01 -1.251840e-01
## 1232  5.722423e-03  4.144214e-02
## 1233  2.519915e-02  6.188494e-02
## 1234 -3.280908e-02  3.315977e-02
## 1235 -4.359277e-02  7.199801e-02
## 1236 -2.103099e-02  6.391153e-02
## 1237  5.309177e-02  2.744901e-02
## 1238  2.480810e-01 -1.119537e-01
## 1239  2.605463e-01 -1.312538e-01
## 1240 -1.753496e-02  7.305405e-02
## 1241  4.474723e-01 -2.459669e-01
## 1242 -2.974094e-01 -3.319485e-01
## 1243  4.594458e-01 -2.460079e-01
## 1244 -1.966586e-01 -1.837357e-01
## 1245 -9.314564e-03  6.498739e-02
## 1246 -1.536578e-01 -1.309862e-01
## 1247  4.234194e-01 -2.408254e-01
## 1248 -3.034367e-01 -3.449286e-01
## 1249 -3.797531e-02  6.075313e-02
## 1250 -1.322472e-01 -8.378165e-02
## 1251 -3.159047e-03  9.067601e-02
## 1252 -9.329882e-04  7.380020e-02
## 1253 -4.330935e-02  3.375371e-02
## 1254 -2.350289e-01 -2.269259e-01
## 1255 -3.467505e-02  5.631845e-02
## 1256  1.116165e-01  2.873891e-02
## 1257 -8.154785e-02 -2.401711e-02
## 1258 -2.322885e-01 -2.183199e-01
## 1259 -1.049753e-02  7.613853e-02
## 1260  4.827049e-02  9.266359e-02
## 1261 -7.792496e-02 -1.608921e-02
## 1262 -5.643808e-03  8.414899e-02
## 1263 -1.298027e-01 -1.076246e-01
## 1264 -2.974107e-01 -3.413219e-01
## 1265 -2.695757e-01 -3.051128e-01
## 1266 -1.941054e-02  7.364267e-02
## 1267  6.538857e-03  8.893804e-02
## 1268 -1.741266e-02  6.364251e-02
## 1269 -1.839585e-02  7.602549e-02
## 1270 -6.887158e-02  3.470718e-02
## 1271  1.732636e-02 -2.412421e-02
## 1272 -2.291497e-01 -2.382751e-01
## 1273 -3.628490e-01 -4.345692e-01
## 1274 -1.222667e-01 -5.722811e-02
## 1275  1.699987e-01 -4.478670e-02
## 1276 -1.096994e-01 -5.925302e-02
## 1277  2.208824e-02  7.573833e-02
## 1278 -2.027537e-03  8.147042e-02
## 1279  2.357667e-03  7.915237e-02
## 1280 -2.024796e-02  6.752395e-02
## 1281 -1.188621e-02  8.892574e-02
## 1282  3.215952e-01 -1.676749e-01
## 1283 -1.091569e-02  8.741967e-02
## 1284 -3.182030e-03  8.913989e-02
## 1285 -1.526384e-02  8.148489e-02
## 1286 -2.018182e-02  6.445906e-02
## 1287 -2.676332e-02  5.719728e-02
## 1288 -7.577752e-02  5.181091e-03
## 1289 -2.082843e-02  7.122249e-02
## 1290 -1.654858e-02  6.761078e-02
## 1291 -3.642764e-02  4.042604e-02
## 1292  2.486137e-01 -1.008015e-01
## 1293  4.995021e-03  6.202247e-02
## 1294 -9.093939e-02 -3.428136e-02
## 1295 -2.299968e-01 -2.420129e-01
## 1296 -1.612549e-02  7.869490e-02
## 1297 -1.683805e-02  6.784553e-02
## 1298 -2.599128e-02  5.942698e-02
## 1299  1.273986e-02  1.100067e-01
## 1300 -1.858819e-02  6.681568e-02
## 1301  6.967584e-02  5.168490e-02
## 1302 -2.165269e-02  4.590507e-02
## 1303  2.395751e-01 -6.371106e-02
## 1304  4.150866e-02  3.359106e-02
## 1305 -3.400140e-02  5.073530e-02
## 1306 -1.534226e-01 -1.487662e-01
## 1307 -6.496089e-02  5.761423e-03
## 1308  1.007011e-01 -3.808068e-02
## 1309  1.548873e-03  9.197634e-02
## 1310 -6.035351e-02  2.741526e-02
## 1311 -2.668613e-02  4.102915e-02
## 1312 -1.312418e-02  5.262286e-02
## 1313 -1.852398e-01 -1.822380e-01
## 1314  1.644313e-01  3.076292e-02
## 1315 -3.259150e-01 -3.815624e-01
## 1316 -2.723026e-01 -3.144584e-01
## 1317 -2.772976e-02  4.684833e-02
## 1318 -1.490731e-02  7.018155e-02
## 1319 -1.668690e-01 -1.474678e-01
## 1320 -1.008570e-01 -5.074862e-02
## 1321 -1.433026e-02  6.385696e-02
## 1322  2.643741e-01 -1.108165e-01
## 1323  8.061869e-02 -2.197360e-02
## 1324  2.609611e-01 -1.533314e-01
## 1325  1.505447e-01 -5.714241e-02
## 1326  1.757863e-01 -5.782009e-02
## 1327  2.203803e-01 -1.026414e-01
## 1328 -2.416957e-01 -2.732107e-01
## 1329  3.672341e-01 -1.941018e-01
## 1330 -1.774472e-02  6.348451e-02
## 1331 -6.546431e-02 -3.731765e-03
## 1332 -2.448466e-02  6.667573e-02
## 1333 -2.491975e-01 -2.722786e-01
## 1334 -2.410089e-02  5.567095e-02
## 1335 -5.387265e-02  3.004767e-02
## 1336 -7.848849e-02  9.255586e-03
## 1337 -4.120571e-02  4.958143e-02
## 1338 -2.027422e-02  6.340820e-02
## 1339  6.635897e-03  9.135022e-02
## 1340 -9.889259e-03  9.089125e-02
## 1341  4.956816e-02  7.683768e-02
## 1342  1.844942e-01 -8.662394e-02
## 1343 -1.211978e-02  8.910029e-02
## 1344 -1.184358e-01 -8.782473e-02
## 1345  4.042411e-03  9.593774e-02
## 1346 -1.821050e-01 -1.774003e-01
## 1347 -6.311631e-03  8.012719e-02
## 1348 -8.042518e-03  8.418649e-02
## 1349 -2.824148e-01 -3.216670e-01
## 1350 -2.548500e-02  5.810039e-02
## 1351 -1.781507e-02  6.813901e-02
## 1352 -2.007510e-01 -2.020794e-01
## 1353  5.173493e-02  7.964132e-02
## 1354  4.002036e-02  1.870145e-02
## 1355  1.241474e-01 -5.725630e-02
## 1356 -2.406673e-02  6.588015e-02
## 1357 -2.774735e-01 -3.193928e-01
## 1358 -8.192134e-03  7.276044e-02
## 1359 -2.843496e-01 -3.359038e-01
## 1360 -6.019946e-02  1.391481e-02
## 1361 -1.156218e-02  7.071693e-02
## 1362 -1.731312e-01 -1.613121e-01
## 1363  3.275027e-02  1.123501e-01
## 1364 -2.465019e-03  8.221320e-02
## 1365 -1.425739e-02  7.779683e-02
## 1366 -8.055664e-02 -2.939782e-02
## 1367  3.216667e-02  9.090600e-02
## 1368 -1.908530e-01 -1.926424e-01
## 1369  1.298084e-01  4.778353e-02
## 1370 -3.041391e-02  5.502359e-02
## 1371 -2.265933e-02  4.993884e-02
## 1372  5.095418e-02  5.218967e-03
## 1373 -8.058616e-02 -3.116325e-02
## 1374 -2.636433e-02  6.362987e-02
## 1375  2.906363e-01 -1.800762e-01
## 1376  3.450210e-03  1.084354e-01
## 1377  1.495329e-01 -5.730906e-02
## 1378 -1.867790e-02  6.776242e-02
## 1379 -2.892881e-02  5.034498e-02
## 1380 -1.589964e-02  8.108346e-02
## 1381 -3.251112e-02  5.895684e-02
## 1382 -1.283815e-02  6.656616e-02
## 1383 -7.295826e-02 -1.775235e-02
## 1384 -1.788720e-02  5.443124e-02
## 1385  8.138789e-02 -5.155385e-03
## 1386  1.523027e-01  1.468932e-02
## 1387 -2.834456e-02  5.494061e-02
## 1388 -1.765204e-01 -1.441217e-01
## 1389 -8.503737e-03  5.434576e-02
## 1390 -5.930185e-02  1.549501e-02
## 1391  2.114055e-01 -1.172628e-01
## 1392 -2.719279e-01 -3.089050e-01
## 1393 -1.293798e-01 -1.056890e-01
## 1394  3.323393e-01 -1.634033e-01
## 1395 -3.005538e-02  3.982921e-02
## 1396 -1.204923e-01 -7.594711e-02
## 1397  2.418420e-01 -1.141902e-01
## 1398  1.402841e-02  6.694270e-02
## 1399 -1.516757e-02  6.766701e-02
## 1400 -3.414825e-02  4.774859e-02
## 1401 -2.674625e-02  6.057302e-02
## 1402  2.744042e-01 -1.565974e-01
## 1403 -1.606414e-02  6.410707e-02
## 1404 -5.409990e-02  2.105998e-02
## 1405  2.309074e-02  8.369319e-02
## 1406 -3.185163e-02  6.102805e-02
## 1407  1.134128e-02  4.547275e-02
## 1408 -1.540867e-01 -1.351431e-01
## 1409  5.625092e-02  4.491535e-02
## 1410  3.064274e-01 -1.490560e-01
## 1411 -1.014546e-02  6.456143e-02
## 1412 -3.956586e-02  2.997243e-02
## 1413 -2.864275e-02  5.085985e-02
## 1414 -4.864818e-02  4.384956e-03
## 1415 -1.570747e-02  7.507154e-02
## 1416  2.003280e-02  6.385548e-02
## 1417 -6.939591e-03  1.000525e-01
## 1418 -2.812184e-01 -3.053311e-01
## 1419  3.586106e-01 -1.835743e-01
## 1420 -5.106471e-02  3.062087e-02
## 1421  8.291676e-02  3.860775e-02
## 1422  3.057697e-03  1.067937e-01
## 1423 -1.175839e-01 -7.466063e-02
## 1424  1.228720e-01  5.001106e-02
## 1425 -8.053883e-02  3.115419e-02
## 1426 -1.660839e-01 -1.342816e-01
## 1427 -3.114393e-03  8.573642e-02
## 1428  1.517352e-01 -1.705021e-02
## 1429 -1.638348e-01 -1.042179e-01
## 1430 -5.151520e-02  4.788048e-02
## 1431  1.313365e-01  1.353006e-02
## 1432  4.192603e-01 -2.469819e-01
## 1433 -2.601819e-01 -2.815400e-01
## 1434  1.626623e-02  6.532916e-02
## 1435  1.083800e-01 -2.420486e-02
## 1436 -2.959770e-02  5.985606e-02
## 1437 -1.212609e-01 -7.121917e-02
## 1438 -2.028423e-01 -2.206768e-01
## 1439 -8.460039e-02 -2.081769e-02
## 1440 -2.783416e-01 -3.238234e-01
## 1441 -1.388201e-01 -5.755167e-02
## 1442 -3.560643e-02  4.994232e-02
## 1443 -6.992433e-02  2.020520e-02
## 1444  3.019779e-01 -1.762046e-01
## 1445 -3.960333e-02  4.026388e-02
## 1446 -8.542110e-02 -3.969742e-02
## 1447 -1.841867e-02  5.773112e-02
## 1448  4.615382e-02  9.652376e-02
## 1449  3.898281e-01 -2.178989e-01
## 1450 -2.280919e-01 -2.410577e-01
## 1451 -2.608900e-02  6.428377e-02
## 1452  1.937214e-01 -8.791876e-02
## 1453 -9.063821e-02 -4.225344e-02
## 1454 -5.854633e-02  3.101557e-02
## 1455 -2.203267e-02  5.778575e-02
## 1456  3.493697e-02  9.889625e-02
## 1457 -3.745986e-02  4.263277e-02
## 1458  1.516149e-01 -8.370706e-02
## 1459 -3.127120e-01 -3.771425e-01
## 1460 -1.800741e-01 -1.848890e-01
## 1461 -1.227321e-02  8.908554e-02
## 1462 -1.302964e-01 -9.450271e-02
## 1463 -6.669207e-02 -2.364776e-03
## 1464 -4.664285e-02  4.074829e-02
## 1465 -2.144816e-01 -2.110102e-01
## 1466 -8.343128e-02 -1.016792e-02
## 1467 -1.047960e-02  7.295490e-02
## 1468 -3.119195e-02  5.995745e-02
## 1469 -3.386773e-03  7.133151e-02
## 1470 -6.279062e-02  1.657180e-02
## 1471 -2.442223e-02  6.591411e-02
## 1472 -1.205190e-01 -7.842977e-02
## 1473  5.039375e-02  5.124622e-02
## 1474 -7.973449e-02 -3.254220e-02
## 1475 -4.456551e-02  4.710687e-02
## 1476 -1.215702e-01 -9.902489e-02
## 1477 -1.582532e-01 -1.606685e-01
## 1478 -1.483859e-02  6.466257e-02
## 1479 -9.216925e-02 -4.981745e-02
## 1480 -1.520600e-01 -1.395324e-01
## 1481 -1.010955e-01 -3.642569e-02
## 1482 -2.096845e-01 -2.087200e-01
## 1483 -2.414902e-01 -2.544477e-01
## 1484 -2.287423e-01 -2.555161e-01
## 1485 -2.538952e-02  7.383314e-02
## 1486 -1.936571e-01 -1.963940e-01
## 1487 -4.250535e-02  2.433330e-02
## 1488 -3.218549e-02  5.937850e-02
## 1489 -2.566344e-01 -2.888500e-01
## 1490  1.205315e-03  7.934157e-02
## 1491 -2.295171e-02  6.497767e-02
## 1492  3.476407e-03  1.063478e-01
## 1493 -1.440817e-02  8.696366e-02
## 1494 -3.195122e-02  4.349889e-02
## 1495 -1.176974e-02  8.940357e-02
## 1496 -1.020867e-02  8.535214e-02
## 1497  1.031961e-02  1.044712e-01
## 1498 -9.186932e-02 -2.945933e-02
## 1499 -1.502959e-02  8.821867e-02
## 1500  2.222834e-03  8.404692e-02
## 1501  1.085504e-01 -2.811101e-02
## 1502 -3.988702e-02  2.788614e-02
## 1503 -1.756063e-02  7.683453e-02
## 1504 -2.264415e-01 -2.463592e-01
## 1505  2.763876e-01 -1.439271e-01
## 1506 -1.419697e-02  6.430093e-02
## 1507 -2.461337e-02  7.888816e-02
## 1508 -2.632957e-02  4.492061e-02
## 1509 -3.707272e-02  4.533645e-02
## 1510 -1.012475e-02  7.557532e-02
## 1511  7.180061e-03  3.973907e-02
## 1512 -1.567073e-01 -1.417943e-01
## 1513 -5.701464e-02  3.427055e-02
## 1514 -4.749338e-02  3.982312e-02
## 1515 -2.367590e-02  6.864683e-02
## 1516 -4.272039e-02  3.709598e-02
## 1517 -1.718275e-01 -1.602163e-01
## 1518 -1.040263e-02  8.700434e-02
## 1519 -5.928222e-02  2.414974e-02
## 1520 -4.327824e-02  5.870657e-02
## 1521  3.516354e-01 -2.057041e-01
## 1522  7.460409e-02  7.178265e-02
## 1523 -7.380033e-02 -2.085451e-02
## 1524 -8.560381e-02 -3.952593e-02
## 1525 -3.260842e-02  4.251148e-02
## 1526 -1.677667e-02  6.707998e-02
## 1527  7.735144e-02  1.888934e-02
## 1528 -3.759523e-02  3.826368e-02
## 1529  1.636915e-02  1.540044e-02
## 1530 -2.773039e-02  3.463435e-02
## 1531  7.361602e-02  4.387085e-02
## 1532 -1.227201e-01 -8.562864e-02
## 1533 -8.119785e-03  6.871431e-02
## 1534  5.810885e-03  7.821181e-02
## 1535 -7.383859e-02  4.496504e-03
## 1536  5.507333e-02  3.426889e-02
## 1537 -9.286829e-02 -5.226981e-02
## 1538 -3.129951e-01 -3.687905e-01
## 1539  1.966917e-01 -1.004750e-01
## 1540  4.045844e-01 -1.943286e-01
## 1541 -1.760383e-01 -1.176504e-01
## 1542  1.977140e-01 -9.728184e-02
## 1543 -9.112229e-03  6.904352e-02
## 1544  8.732897e-02  4.796637e-02
## 1545 -1.786460e-02  6.816194e-02
## 1546  1.068589e-01 -9.219018e-03
## 1547 -1.709721e-02  5.994920e-02
## 1548 -9.546910e-03  7.153683e-02
## 1549  3.044286e-01 -1.319590e-01
## 1550 -2.507643e-02  6.980790e-02
## 1551 -2.848844e-02  5.863263e-02
## 1552  2.611551e-01 -1.175078e-01
## 1553 -2.211836e-01 -2.327609e-01
## 1554 -6.248005e-03  7.201546e-02
## 1555 -1.939653e-01 -2.045942e-01
## 1556  2.502266e-02  9.134331e-02
## 1557  8.114156e-02  4.646069e-02
## 1558 -3.048199e-01 -3.550116e-01
## 1559 -4.118402e-02  2.850878e-02
## 1560 -2.279685e-02  7.334446e-02
## 1561 -1.036511e-01 -4.679483e-02
## 1562  2.690989e-02  9.256448e-02
## 1563 -5.778014e-02  1.983220e-02
## 1564 -4.948050e-02  3.352346e-02
## 1565 -1.088303e-02  9.744872e-02
## 1566 -6.248246e-02  5.959470e-03
## 1567 -1.410409e-02  1.077322e-01
## 1568  1.208789e-01  2.007356e-02
## 1569 -9.060317e-03  8.759425e-02
## 1570  1.001467e-01 -1.055253e-02
## 1571 -7.523037e-02 -1.397782e-02
## 1572  1.766525e-02  9.238185e-02
## 1573 -9.170413e-03  9.645459e-02
## 1574 -1.729268e-02  7.220221e-02
## 1575 -9.234090e-02 -5.270957e-02
## 1576  3.298021e-01 -2.193674e-01
## 1577 -1.937058e-02  6.589756e-02
## 1578 -1.193816e-01 -8.200874e-02
## 1579  6.927282e-02  3.215468e-02
## 1580 -4.176886e-02  5.990831e-02
## 1581 -2.309130e-02  6.751119e-02
## 1582  3.411711e-02  9.824588e-02
## 1583 -8.754505e-02 -3.396668e-02
## 1584  1.156036e-01 -3.979046e-03
## 1585 -1.570031e-01 -1.447818e-01
## 1586  7.345600e-02  1.216247e-02
## 1587  3.120558e-02  5.560274e-02
## 1588 -7.232316e-02  3.951557e-02
## 1589 -1.998741e-01 -1.789115e-01
## 1590 -3.319626e-01 -4.050515e-01
## 1591 -9.424739e-03  8.771179e-02
## 1592  6.211435e-02  9.919579e-02
## 1593 -1.998787e-01 -1.759388e-01
## 1594 -1.353584e-01 -1.207567e-01
## 1595 -1.572623e-01 -1.394352e-01
## 1596 -2.768093e-02  3.256235e-02
## 1597  4.733913e-02  6.756831e-02
## 1598 -3.175869e-02  5.364394e-02
## 1599 -1.274779e-02  4.379332e-02
## 1600 -6.633627e-02 -1.087260e-03
## 1601 -9.939480e-03  9.040806e-02
## 1602  5.902613e-02  9.786659e-02
## 1603 -1.716803e-01 -1.678306e-01
## 1604  3.069688e-02  1.158968e-01
## 1605  2.987588e-01 -1.577687e-01
## 1606  2.090181e-01 -9.876138e-02
## 1607  2.873418e-02  3.612946e-02
## 1608  8.931386e-03  1.113597e-01
## 1609 -3.247701e-02 -6.185833e-03
## 1610 -2.767028e-02  4.900033e-02
## 1611 -2.149708e-02  5.646814e-02
## 1612 -4.682356e-02  6.429506e-02
## 1613 -1.975142e-02  6.980916e-02
## 1614 -2.108574e-01 -2.239547e-01
## 1615  4.140156e-01 -2.377891e-01
## 1616 -3.064853e-01 -3.446950e-01
## 1617 -3.770971e-02  4.730679e-02
## 1618 -7.022699e-02  1.958720e-03
## 1619  2.728362e-01 -1.637521e-01
## 1620 -3.441605e-02  4.603593e-02
## 1621 -6.181158e-02  8.009162e-03
## 1622 -3.782539e-02  3.943937e-02
## 1623 -2.116170e-01 -2.347610e-01
## 1624 -1.077503e-01 -7.062502e-02
## 1625  8.069378e-02  6.748847e-02
## 1626 -5.190908e-02  4.271923e-02
## 1627 -8.959725e-02 -3.986012e-02
## 1628  1.740315e-01  6.320975e-04
## 1629 -6.102101e-02 -1.008815e-02
## 1630 -1.574919e-02  6.766310e-02
## 1631 -1.378716e-01 -1.193222e-01
## 1632 -1.530345e-01 -1.427608e-01
## 1633 -5.430386e-03  5.815606e-02
## 1634 -1.121259e-02  1.062399e-01
## 1635 -9.592099e-02 -3.275866e-02
## 1636 -7.629468e-03  1.044014e-01
## 1637 -1.067929e-02  8.388384e-02
## 1638  2.850549e-02  7.565298e-02
## 1639 -3.221717e-01 -3.859722e-01
## 1640 -1.837411e-02  6.921609e-02
## 1641  1.829252e-02  9.594420e-02
## 1642 -6.257934e-03  1.175421e-01
## 1643 -7.572820e-02  5.939848e-03
## 1644 -3.785232e-02  6.568035e-02
## 1645 -1.917501e-02  5.525228e-02
## 1646 -2.708196e-02  5.924209e-02
## 1647  9.782255e-02 -2.651608e-02
## 1648 -1.821678e-02  5.611950e-02
## 1649 -1.290104e-03  7.439098e-02
## 1650  1.742447e-01 -6.527440e-02
## 1651  1.627808e-01 -5.283360e-02
## 1652  3.438012e-01 -1.969545e-01
## 1653 -3.002015e-02  3.272259e-02
## 1654 -2.293056e-02  7.161201e-02
## 1655  1.974146e-02  1.134269e-01
## 1656  3.292246e-01 -1.851691e-01
## 1657 -5.781403e-02 -3.446403e-03
## 1658 -1.325940e-01 -1.040546e-01
## 1659 -1.050105e-02  5.439322e-02
## 1660 -7.543184e-02 -1.046680e-02
## 1661  1.046103e-01 -2.610760e-02
## 1662 -4.385149e-02  3.977387e-02
## 1663 -9.352238e-03  9.141430e-02
## 1664 -9.551956e-02 -1.442252e-02
## 1665 -1.691687e-02  6.492764e-02
## 1666 -2.929003e-02  5.458844e-02
## 1667  4.411196e-02  1.068888e-01
## 1668 -8.206032e-02 -3.212132e-02
## 1669 -4.687486e-02  3.849974e-02
## 1670 -1.765939e-02  7.225424e-02
## 1671  3.147455e-01 -1.553012e-01
## 1672 -2.958199e-01 -3.499997e-01
## 1673 -1.685400e-02  6.171461e-02
## 1674  3.375838e-01 -1.371840e-01
## 1675  5.561663e-02  8.164651e-02
## 1676 -2.171171e-03  5.434510e-02
## 1677 -2.185195e-01 -2.239968e-01
## 1678 -7.949190e-02 -6.944215e-03
## 1679  2.179038e-04  4.542747e-02
## 1680  3.081499e-01 -1.775907e-01
## 1681 -2.769273e-01 -3.291808e-01
## 1682 -2.687632e-02  6.027160e-02
## 1683  2.575371e-01 -1.109474e-01
## 1684 -1.332484e-02  7.764803e-02
## 1685 -3.954156e-02  4.746443e-02
## 1686 -1.122037e-02  9.594893e-02
## 1687 -4.688006e-02  2.354402e-02
## 1688 -1.943813e-01 -1.904713e-01
## 1689 -6.082181e-02  2.782893e-02
## 1690  2.808759e-01 -7.055566e-02
## 1691 -2.201629e-01 -2.389232e-01
## 1692  1.751619e-03  1.250024e-01
## 1693  3.295502e-01 -1.284753e-01
## 1694 -3.709172e-02  4.761755e-02
## 1695 -4.960725e-02  3.728299e-02
## 1696 -1.350191e-01 -8.982980e-02
## 1697 -4.444852e-02  6.172133e-02
## 1698 -1.047450e-01 -4.856788e-02
## 1699  2.491948e-01 -9.395050e-02
## 1700 -1.102692e-01 -4.662547e-02
## 1701 -1.275510e-02  7.385307e-02
## 1702  3.941537e-01 -2.184583e-01
## 1703  1.121692e-02  1.157247e-01
## 1704 -1.549531e-01 -1.498130e-01
## 1705 -1.486807e-01 -1.218813e-01
## 1706 -1.397269e-02  8.796256e-02
## 1707 -4.911632e-03  1.033605e-01
## 1708 -1.092546e-01 -8.780833e-02
## 1709  4.558055e-03  8.697278e-02
## 1710 -8.421627e-02 -3.599306e-02
## 1711 -6.739913e-02  6.085782e-03
## 1712 -4.023498e-02  4.613427e-02
## 1713  2.464878e-01 -6.270126e-02
## 1714 -2.206788e-01 -2.335101e-01
## 1715  3.292966e-01 -2.043042e-01
## 1716 -6.437845e-03  9.566081e-02
## 1717 -1.426189e-02  9.636652e-02
## 1718 -5.559783e-02  1.887410e-02
## 1719 -2.641078e-01 -3.127946e-01
## 1720 -2.834834e-02  5.960803e-02
## 1721 -2.593982e-01 -2.865931e-01
## 1722  3.399637e-01 -1.922362e-01
## 1723 -9.332667e-02 -5.736200e-02
## 1724 -1.671005e-01 -1.624311e-01
## 1725 -1.233333e-02  9.252070e-02
## 1726 -5.951371e-02  8.021299e-03
## 1727 -3.514998e-02  5.784534e-02
## 1728 -6.835641e-03  9.472342e-02
## 1729  7.877330e-02 -2.708817e-03
## 1730 -1.402688e-02  6.662557e-02
## 1731  4.211818e-02  9.284083e-02
## 1732  1.735750e-02  6.542226e-02
## 1733 -5.098672e-03  1.059140e-01
## 1734 -1.891372e-02  6.957942e-02
## 1735 -2.377345e-02  5.267162e-02
## 1736 -2.386680e-02  4.865660e-02
## 1737 -5.791168e-02  3.349494e-02
## 1738 -2.517777e-02  6.290073e-02
## 1739 -1.099732e-02  8.311384e-02
## 1740  1.619078e-01  4.547272e-03
## 1741  3.709709e-01 -1.894751e-01
## 1742  3.371542e-01 -2.098101e-01
## 1743  1.806982e-02  1.125950e-01
## 1744  9.027718e-02 -5.385820e-03
## 1745 -5.465768e-02  2.731527e-03
## 1746 -5.237903e-02  4.029785e-03
## 1747  3.471878e-03  7.172029e-02
## 1748 -7.661959e-02 -7.084012e-04
## 1749 -1.870728e-02  6.627284e-02
## 1750 -1.819241e-01 -1.836372e-01
## 1751 -3.706114e-02  4.243342e-02
## 1752 -1.914991e-02  6.268942e-02
## 1753 -1.969746e-01 -1.993680e-01
## 1754 -8.233288e-02 -1.072454e-02
## 1755 -1.948307e-01 -2.043665e-01
## 1756 -1.660793e-01 -1.640292e-01
## 1757 -1.552776e-02  6.968701e-02
## 1758 -6.030744e-02  6.089099e-03
## 1759 -3.475002e-02  4.213863e-02
## 1760 -4.081097e-02  4.205394e-02
## 1761  7.655059e-03  9.995931e-02
## 1762 -2.898339e-01 -3.483736e-01
## 1763 -2.400701e-01 -2.625734e-01
## 1764 -2.558040e-02  6.035220e-02
## 1765 -1.794725e-02  6.579181e-02
## 1766 -1.654869e-02  6.310815e-02
## 1767 -1.500792e-02  5.386101e-02
## 1768  2.450340e-03  1.140822e-01
## 1769 -1.431122e-02  6.961270e-02
## 1770 -3.272388e-02  5.065053e-02
## 1771 -1.837582e-02  6.557008e-02
## 1772  3.694245e-01 -2.039375e-01
## 1773 -8.797374e-03  7.893493e-02
## 1774 -1.807122e-01 -1.853375e-01
## 1775  4.012084e-02  1.021926e-01
## 1776  1.094432e-01 -2.591597e-02
## 1777  2.868714e-01 -1.036040e-01
## 1778  2.800663e-02  1.381647e-01
## 1779  1.824004e-03  7.148841e-02
## 1780 -1.178351e-01 -6.418413e-02
## 1781  3.673839e-01 -2.254619e-01
## 1782 -3.033179e-02  5.036330e-02
## 1783 -2.613095e-02  5.440757e-02
## 1784 -3.027487e-02  9.161290e-04
## 1785 -1.620897e-02  6.631074e-02
## 1786 -1.562372e-01 -1.502148e-01
## 1787 -2.516234e-01 -2.843041e-01
## 1788 -4.650256e-02  1.460677e-02
## 1789 -2.883350e-02  6.359348e-02
## 1790  3.323128e-01 -1.921676e-01
## 1791 -1.869869e-01 -1.941142e-01
## 1792 -2.117234e-01 -2.203391e-01
## 1793 -1.175327e-01 -9.468572e-02
## 1794 -4.591855e-02  2.602411e-02
## 1795  1.577667e-01 -5.845561e-02
## 1796  3.054757e-01 -1.575151e-01
## 1797 -2.091097e-01 -2.046185e-01
## 1798 -1.695358e-01 -1.485509e-01
## 1799  3.192768e-03  1.060386e-01
## 1800  1.591186e-01  2.152684e-02
## 1801  1.474450e-01  1.892879e-03
## 1802 -1.211483e-01 -9.218901e-02
## 1803 -3.194396e-02  4.747426e-02
## 1804  3.009395e-03  1.173875e-01
## 1805 -1.826209e-01 -1.889867e-01
## 1806 -1.103869e-01 -6.146382e-02
## 1807 -3.176718e-03  8.643052e-02
## 1808 -1.231364e-02  8.958235e-02
## 1809 -2.051137e-02  7.046074e-02
## 1810 -3.185262e-02  5.179857e-02
## 1811 -5.669312e-03  7.486682e-02
## 1812 -9.892915e-03  1.018191e-01
## 1813 -6.508972e-02  1.103587e-03
## 1814 -1.839187e-02  5.457885e-02
## 1815  3.629786e-03  1.192355e-01
## 1816 -9.170522e-03  1.236761e-01
## 1817 -1.147030e-02  8.347787e-02
## 1818  1.112019e-01  1.341234e-02
## 1819  2.750784e-02  7.341370e-02
## 1820 -6.941822e-03  9.960994e-02
## 1821 -9.858916e-02 -2.971253e-02
## 1822  3.484501e-02  7.556571e-02
## 1823  6.299948e-02  6.700446e-02
## 1824 -2.799877e-02  5.373488e-02
## 1825 -3.573804e-02  4.424152e-02
## 1826 -1.816732e-02  6.582265e-02
## 1827 -2.004049e-02  5.413120e-02
## 1828 -2.287648e-03  9.681929e-02
## 1829 -2.166493e-02  6.998086e-02
## 1830  2.608920e-01 -1.390669e-01
## 1831  3.613396e-01 -2.124471e-01
## 1832 -2.512409e-02  4.660041e-02
## 1833 -4.751183e-02  3.697304e-02
## 1834  2.231024e-02  5.892375e-02
## 1835  1.053260e-01 -3.083026e-02
## 1836 -8.255399e-03  6.460383e-02
## 1837  2.321300e-02  1.062751e-01
## 1838  8.482056e-03  7.648071e-02
## 1839  4.269544e-02  7.158113e-02
## 1840 -7.088398e-02 -2.048306e-02
## 1841  2.822705e-01 -1.397232e-01
## 1842 -8.689224e-03  9.484853e-02
## 1843 -4.452114e-04  6.107592e-02
## 1844 -7.100361e-04  2.645934e-02
## 1845  6.855072e-02  5.776646e-02
## 1846 -5.360017e-02  4.763867e-02
## 1847 -9.691169e-02 -1.647833e-02
## 1848 -2.553908e-01 -2.740299e-01
## 1849 -1.131455e-02  6.483580e-02
## 1850  1.731106e-01 -1.096650e-01
## 1851 -4.819867e-02  1.706331e-02
## 1852  3.434613e-01 -1.895497e-01
## 1853  1.238380e-01  6.157292e-02
## 1854 -1.594672e-01 -1.353548e-01
## 1855 -4.931100e-02  5.459357e-02
## 1856 -7.432241e-03  9.027591e-02
## 1857 -2.511172e-02  6.050388e-02
## 1858 -8.738237e-03  9.744919e-02
## 1859  5.468467e-02  6.320518e-02
## 1860 -1.249361e-01 -8.152530e-02
## 1861  4.226370e-03  1.015143e-01
## 1862 -3.215634e-02  5.240916e-02
## 1863  2.723083e-01 -1.085062e-01
## 1864  7.239776e-03  5.612357e-02
## 1865 -6.238905e-03  1.136370e-01
## 1866 -1.928699e-02  8.544547e-02
## 1867 -2.412255e-01 -2.634160e-01
## 1868 -9.800544e-02 -6.485314e-02
## 1869 -3.269615e-02  5.297411e-02
## 1870 -1.799862e-02  6.910617e-02
## 1871 -9.908670e-03  6.101676e-02
## 1872 -4.722385e-02  3.963964e-02
## 1873  2.570620e-03  1.317253e-01
## 1874 -1.850437e-02  6.554825e-02
## 1875  3.499151e-02  7.113140e-02
## 1876  1.885527e-02  1.064614e-01
## 1877 -2.311990e-02  7.915963e-02
## 1878 -2.262506e-02  6.491230e-02
## 1879 -2.863397e-02  5.888444e-02
## 1880 -1.533069e-02  6.661298e-02
## 1881 -8.422329e-02 -4.324513e-02
## 1882 -1.161120e-02  8.444611e-02
## 1883 -1.553371e-02  4.961881e-02
## 1884 -1.372999e-02  5.838210e-02
## 1885 -2.415756e-02  9.051411e-02
## 1886 -1.455249e-02  7.261730e-02
## 1887  3.300360e-01 -2.040143e-01
## 1888 -2.329606e-02  5.628460e-02
## 1889 -1.330472e-02  7.834149e-02
## 1890 -9.533627e-02 -3.458844e-02
## 1891  2.590575e-02  1.031017e-01
## 1892 -2.876205e-02  4.784556e-02
## 1893  2.645211e-01 -1.234118e-01
## 1894 -5.381873e-02  3.294737e-03
## 1895  2.088840e-01 -9.634250e-02
## 1896  3.130218e-02  1.090198e-01
## 1897  1.308829e-01 -1.661047e-03
## 1898 -8.284942e-02  7.879970e-03
## 1899 -2.446000e-02  8.083093e-02
## 1900 -3.466910e-02  4.909343e-02
## 1901 -5.148891e-02  1.714516e-02
## 1902  4.876569e-03  9.672064e-02
## 1903 -2.693637e-02  5.377883e-02
## 1904 -2.526163e-01 -2.926654e-01
## 1905  1.751167e-02  9.314073e-02
## 1906 -1.140258e-02  8.429597e-02
## 1907  3.906706e-02  1.300472e-01
## 1908 -7.023892e-03  4.761876e-02
## 1909 -1.963038e-01 -1.999521e-01
## 1910  1.773652e-01 -2.094370e-03
## 1911 -2.213863e-02  6.743038e-02
## 1912  8.406897e-03  1.159286e-01
## 1913 -1.511829e-01 -1.364040e-01
## 1914 -2.168591e-02  7.318188e-02
## 1915  6.406499e-02  5.764358e-02
## 1916 -2.629276e-02  6.640640e-02
## 1917 -1.705710e-02  6.397236e-02
## 1918 -1.568495e-02  7.237473e-02
## 1919  2.331552e-02  4.038672e-02
## 1920 -6.127099e-02  2.444578e-02
## 1921 -1.451527e-02  8.298664e-02
## 1922 -1.219026e-02  6.519458e-02
## 1923 -2.501666e-01 -2.829737e-01
## 1924  3.160352e-01 -1.867906e-01
## 1925  1.989757e-01 -9.734232e-02
## 1926 -1.991150e-02  7.725804e-02
## 1927 -1.060655e-02  8.460871e-02
## 1928  3.530184e-01 -1.862714e-01
## 1929  5.280107e-02  2.616283e-02
## 1930 -1.587897e-02  5.271124e-02
## 1931 -4.749947e-02  1.416062e-02
## 1932 -4.331579e-02  1.079889e-02
## 1933  5.643886e-02  8.303221e-02
## 1934 -2.312854e-02  6.133135e-02
## 1935 -2.102089e-02  5.931694e-02
## 1936 -1.063372e-02  5.848625e-02
## 1937 -4.334647e-02  2.038880e-02
## 1938  1.785304e-03  1.099692e-01
## 1939 -2.632854e-01 -3.043446e-01
## 1940 -1.541799e-02  6.327208e-02
## 1941 -9.113336e-02 -1.126257e-02
## 1942 -4.126781e-02  6.096404e-02
## 1943 -1.093326e-01 -4.430625e-02
## 1944 -5.080812e-02  3.582337e-02
## 1945 -3.354061e-02  4.446277e-02
## 1946 -1.538694e-02  6.429946e-02
## 1947  2.688315e-01 -1.099596e-01
## 1948 -5.038809e-02  2.579561e-02
## 1949 -4.644269e-02  3.411026e-02
## 1950 -4.365772e-02  2.668715e-02
## 1951 -9.928066e-02 -5.587261e-02
## 1952 -2.237700e-01 -2.336024e-01
## 1953 -1.146787e-01 -8.953962e-02
## 1954  1.404082e-02  9.870896e-02
## 1955 -2.128210e-02  6.062864e-02
## 1956  6.410739e-02  5.630640e-02
## 1957 -1.428641e-02  7.786611e-02
## 1958 -5.451271e-02  3.911565e-02
## 1959 -3.230346e-01 -3.857670e-01
## 1960 -1.402510e-02  8.311625e-02
## 1961 -2.945214e-02  6.145501e-02
## 1962 -2.792531e-02  7.186642e-02
## 1963  8.715774e-02  7.392983e-02
## 1964 -2.690973e-01 -3.110013e-01
## 1965 -1.074799e-01 -6.001354e-02
## 1966  1.288658e-02  9.898317e-02
## 1967 -5.596445e-03  8.615667e-02
## 1968 -2.848181e-02  7.337783e-02
## 1969 -1.005002e-02  6.784546e-02
## 1970 -2.260047e-02  6.914163e-02
## 1971 -2.528316e-01 -2.911926e-01
## 1972  1.752294e-01 -7.288747e-02
## 1973 -1.071641e-01 -7.926528e-02
## 1974 -2.242943e-01 -2.377120e-01
## 1975 -1.238538e-02  1.034662e-01
## 1976 -1.695398e-02  5.506862e-02
## 1977  4.692432e-01 -2.933351e-01
## 1978  4.113794e-03  1.070034e-01
## 1979 -2.676042e-02  5.529361e-02
## 1980  3.246088e-02  1.208516e-01
## 1981 -1.460099e-02  7.331513e-02
## 1982 -1.611037e-02  6.620608e-02
## 1983 -1.997686e-02  6.870382e-02
## 1984  4.249285e-01 -2.393424e-01
## 1985 -5.671034e-03  9.888151e-02
## 1986 -2.012600e-01 -1.960181e-01
## 1987 -8.785395e-03  8.271041e-02
## 1988 -7.107216e-02  1.527129e-02
## 1989 -3.874549e-02  3.544873e-02
## 1990 -1.643142e-02  7.347472e-02
## 1991 -9.314826e-03  9.408563e-02
## 1992 -1.253236e-01 -9.489760e-02
## 1993 -1.000605e-02  9.691073e-02
## 1994 -2.156626e-03  1.212555e-01
## 1995 -7.640765e-04  9.812354e-02
## 1996 -1.479938e-02  7.732712e-02
## 1997 -1.416231e-02  8.823934e-02
## 1998 -1.638666e-01 -1.382663e-01
## 1999 -1.143473e-02  1.084534e-01
## 2000 -8.444635e-02 -4.055696e-02
## 2001 -1.918121e-02  5.368645e-02
## 2002 -1.480626e-02  8.243253e-02
## 2003 -1.581737e-02  6.248004e-02
## 2004 -3.517233e-02  5.805052e-02
## 2005 -6.113304e-02  7.274143e-03
## 2006 -9.329589e-02 -3.848785e-02
## 2007 -3.865503e-02  6.531002e-02
## 2008 -1.304871e-01 -8.575745e-02
## 2009 -9.036943e-02 -5.056954e-02
## 2010 -1.884575e-02  6.863022e-02
## 2011 -2.015044e-02  5.505615e-02
## 2012  4.971911e-03  7.565641e-02
## 2013  3.955790e-02  1.133454e-01
## 2014 -7.989055e-03  5.790577e-02
## 2015  1.273450e-01  4.011147e-02
## 2016 -5.915864e-03  5.435457e-02
## 2017 -3.368626e-03  1.245091e-01
## 2018 -1.392000e-02  8.460360e-02
## 2019 -3.577500e-02  4.867015e-02
## 2020  5.935816e-02  9.328903e-02
## 2021 -3.058423e-02  6.039176e-02
## 2022 -4.003440e-02  2.604997e-02
## 2023  2.755632e-02  6.971797e-02
## 2024  2.775186e-02  1.124838e-01
## 2025 -8.587324e-02 -3.401182e-02
## 2026 -1.942937e-02  6.455239e-02
## 2027 -1.686199e-01 -1.557750e-01
## 2028 -1.440632e-02  6.236764e-02
## 2029 -3.021205e-02  3.944674e-02
## 2030 -1.369840e-02  9.159185e-02
## 2031 -2.521994e-02  8.557331e-02
## 2032 -2.218401e-02  8.508415e-02
## 2033 -2.737512e-02  4.802400e-02
## 2034 -1.496705e-02  4.642442e-02
## 2035 -4.664324e-02  3.478988e-02
## 2036 -1.030303e-01 -5.030660e-02
## 2037 -1.504150e-02  8.808677e-02
## 2038 -2.545650e-01 -2.873902e-01
## 2039  1.788203e-02  8.527034e-02
## 2040 -9.299452e-02 -3.706952e-02
## 2041 -1.865321e-02  6.266270e-02
## 2042  2.211301e-03  1.346370e-01
## 2043 -7.088094e-03  8.837041e-02
## 2044 -2.380045e-02  4.958414e-02
## 2045  3.660896e-01 -1.968661e-01
## 2046 -6.554261e-02 -1.050120e-02
## 2047  1.582502e-01  1.796759e-02
## 2048 -1.579571e-02  5.550314e-02
## 2049  3.924213e-01 -2.390044e-01
## 2050  2.510322e-01 -1.182411e-01
## 2051  2.813951e-01 -1.352250e-01
## 2052 -2.376909e-02  7.919347e-02
## 2053  2.849219e-02  1.069108e-01
## 2054 -4.205297e-02  4.543896e-02
## 2055  2.941368e-01 -1.549779e-01
## 2056 -5.293679e-02  1.522966e-02
## 2057 -1.129842e-02  7.609997e-02
## 2058  1.066939e-02  6.769474e-02
## 2059 -1.756360e-02  6.339048e-02
## 2060 -8.211892e-02 -3.935827e-02
## 2061 -1.101895e-01 -7.763817e-02
## 2062  7.123643e-02  1.531247e-02
## 2063  4.180273e-01 -2.487862e-01
## 2064  4.511608e-03  8.080422e-02
## 2065 -1.458827e-02  6.602309e-02
## 2066 -1.275262e-02  7.743311e-02
## 2067 -1.578650e-01 -1.245800e-01
## 2068 -2.323553e-01 -2.431213e-01
## 2069 -1.245349e-02  7.040415e-02
## 2070 -6.751169e-03  1.166428e-01
## 2071 -8.950535e-02 -4.330524e-02
## 2072 -4.577419e-02  1.550306e-02
## 2073 -8.867507e-02 -3.882061e-02
## 2074  1.447637e-02  8.870336e-02
## 2075 -1.593826e-02  7.579125e-02
## 2076 -2.151848e-02  5.974270e-02
## 2077  2.155822e-02  1.130195e-01
## 2078 -1.387613e-02  1.015990e-01
## 2079 -2.517931e-02  6.899820e-02
## 2080 -1.062942e-01 -7.091402e-02
## 2081 -1.482199e-02  5.904100e-02
## 2082 -3.489400e-02  6.390993e-02
## 2083 -1.057827e-01 -7.035054e-02
## 2084 -7.806180e-02 -2.171659e-02
## 2085 -2.941868e-02  6.069775e-02
## 2086 -3.464625e-02  3.517197e-02
## 2087 -1.626171e-02  6.911009e-02
## 2088 -1.011277e-02  5.542601e-02
## 2089 -1.394793e-02  8.487384e-02
## 2090 -4.844716e-02  2.402386e-02
## 2091 -7.654107e-03  6.999592e-02
## 2092  2.780151e-01 -1.196128e-01
## 2093 -3.057303e-02  6.900268e-02
## 2094 -3.243923e-02  6.526682e-02
## 2095 -1.799735e-01 -1.579346e-01
## 2096 -9.499701e-02 -4.936947e-02
## 2097 -6.238301e-03  1.244786e-01
## 2098  8.313457e-03  1.219603e-01
## 2099 -2.171531e-02  5.733488e-02
## 2100 -7.229242e-03  9.598698e-02
## 2101 -8.326072e-02 -3.573660e-02
## 2102  3.523425e-01 -2.157581e-01
## 2103  3.506178e-02  1.366769e-01
## 2104  4.031310e-03  8.156723e-02
## 2105 -1.505856e-02  6.392829e-02
## 2106 -3.392035e-02  5.223445e-02
## 2107  2.615341e-01 -1.584458e-01
## 2108  4.457471e-02  6.712652e-02
## 2109  6.517262e-02  8.656674e-02
## 2110 -1.836283e-02  4.134345e-02
## 2111 -6.809760e-03  6.485630e-02
## 2112  9.231903e-03  6.192878e-02
## 2113 -6.931320e-02 -6.822341e-03
## 2114  6.714018e-02  1.009354e-02
## 2115 -1.485270e-02  7.110729e-02
## 2116 -3.223203e-02  6.200215e-02
## 2117 -1.414756e-02  6.320738e-02
## 2118 -1.529751e-03  8.704345e-02
## 2119 -4.552224e-02  2.279407e-02
## 2120  1.596545e-02  9.589646e-02
## 2121 -2.902372e-02  4.334023e-02
## 2122  5.895136e-03  9.468154e-02
## 2123 -8.497182e-03  8.958695e-02
## 2124 -4.714147e-02  3.000219e-02
## 2125 -1.729965e-02  7.986900e-02
## 2126  6.242224e-04  9.587838e-02
## 2127 -6.481304e-03  9.307471e-02
## 2128 -1.385547e-02  8.098566e-02
## 2129 -1.263601e-02  1.057369e-01
## 2130 -1.622887e-02  1.046480e-01
## 2131  8.077060e-02  9.678314e-02
## 2132 -2.511038e-02  8.668009e-02
## 2133 -3.179970e-03  1.167936e-01
## 2134 -1.100139e-02  9.049487e-02
## 2135 -8.394731e-02 -2.175673e-02
## 2136 -2.769325e-02  8.165806e-02
## 2137 -1.775593e-02  7.117108e-02
## 2138 -8.680782e-02 -3.654537e-02
## 2139 -4.370575e-02  2.222784e-02
## 2140 -3.645719e-02  7.267276e-02
## 2141  2.222159e-02  4.077999e-02
## 2142 -2.227365e-02  6.323823e-02
## 2143 -3.389302e-02  4.673573e-02
## 2144 -1.266332e-02  7.443542e-02
## 2145  1.512130e-02  1.258065e-01
## 2146 -1.620964e-02  6.940740e-02
## 2147 -8.654923e-03  9.269386e-02
## 2148 -2.028616e-02  7.530408e-02
## 2149  4.383023e-02  9.656030e-02
## 2150 -1.682837e-02  6.720105e-02
## 2151 -1.640370e-02  6.521451e-02
## 2152 -2.299393e-02  4.373673e-02
## 2153 -2.319491e-02  6.925203e-02
## 2154 -7.614368e-02 -1.284482e-02
## 2155 -5.775777e-03  1.032783e-01
## 2156  1.499841e-01 -4.602970e-04
## 2157  7.521272e-02  8.566415e-02
## 2158 -2.723061e-02  9.699210e-02
## 2159 -8.184251e-03  1.298439e-01
## 2160 -1.151973e-02  7.360453e-02
## 2161 -2.226707e-02  8.649290e-02
## 2162 -7.224423e-03  7.683922e-02
## 2163  6.460392e-03  1.474687e-01
## 2164  2.002763e-01 -1.011570e-01
## 2165  8.045537e-02  1.911425e-02
## 2166  3.831451e-01 -2.050254e-01
## 2167  9.399907e-02  5.382238e-02
## 2168 -6.686636e-03  6.698688e-02
## 2169 -4.685045e-02  3.522842e-02
## 2170  8.943300e-02  1.048902e-01
## 2171 -1.668543e-02  5.461291e-02
## 2172 -3.443241e-02  3.072424e-02
## 2173  3.748977e-01 -2.021282e-01
## 2174 -3.155197e-03  1.041752e-01
## 2175 -5.366454e-02  4.775492e-03
## 2176 -7.323560e-03  6.313916e-02
## 2177 -1.735084e-02  7.336150e-02
## 2178 -1.381466e-01 -1.043570e-01
## 2179 -5.074197e-03  6.760882e-02
## 2180  2.285187e-01 -2.585886e-02
## 2181 -2.793385e-02  5.117596e-02
## 2182 -6.892430e-03  8.032846e-02
## 2183 -4.080386e-02  4.182129e-02
## 2184 -2.069071e-03  1.368944e-01
## 2185 -2.111617e-02  6.487596e-02
## 2186 -9.141742e-02 -4.509822e-02
## 2187 -1.532192e-02  8.231495e-02
## 2188 -5.478028e-02  8.261332e-03
## 2189 -1.480957e-01 -1.171142e-01
## 2190  6.027663e-02  7.207010e-02
## 2191 -1.033862e-01 -6.257750e-02
## 2192 -1.494904e-02  7.820472e-02
## 2193 -2.182548e-02  5.582416e-02
## 2194 -2.231500e-02  6.470177e-02
## 2195 -1.098884e-02  1.004773e-01
## 2196 -1.612121e-02  7.397897e-02
## 2197 -9.988443e-03  7.825201e-02
## 2198 -5.140576e-02  2.284640e-02
## 2199 -2.653946e-02  8.724228e-02
## 2200 -2.098672e-02  6.442556e-02
## 2201 -2.239621e-02  8.240210e-02
## 2202 -1.387690e-02  8.492138e-02
## 2203 -9.395016e-02 -3.714400e-02
## 2204 -1.739777e-02  8.568992e-02
## 2205 -3.829005e-02  5.688476e-02
## 2206  5.595303e-02  7.498557e-02
## 2207 -6.994574e-03  8.796140e-02
## 2208 -9.460981e-02 -3.655581e-02
## 2209 -1.798234e-02  6.624885e-02
## 2210  8.578337e-02  2.861638e-02
## 2211 -3.487410e-02  4.550329e-02
## 2212  3.990292e-02  7.232258e-02
## 2213 -1.216275e-02  7.548964e-02
## 2214 -1.166023e-02  1.050410e-01
## 2215 -3.307946e-02  4.869167e-02
## 2216 -1.466874e-02  1.065296e-01
## 2217  6.623648e-02  9.370337e-02
## 2218 -3.192098e-02  6.038007e-02
## 2219 -1.722061e-02  7.270768e-02
## 2220 -2.685828e-02  6.828420e-02
## 2221 -9.446610e-02 -5.339897e-02
## 2222 -1.237813e-02  7.018027e-02
## 2223 -1.872114e-02  6.520223e-02
## 2224  1.778763e-01 -8.043752e-02
## 2225  3.946906e-02  3.882507e-02
## 2226 -4.810955e-02  1.269468e-02
## 2227 -5.368053e-02  2.787338e-02
## 2228 -2.258827e-02  7.098675e-02
## 2229 -4.718324e-02  3.273909e-02
## 2230 -9.639642e-03  6.676389e-02
## 2231 -2.642756e-02  6.705297e-02
## 2232  1.034779e-01  5.633608e-02
## 2233  3.219177e-01 -1.601036e-01
## 2234  2.763572e-01 -1.470323e-01
## 2235 -1.446343e-02  6.375719e-02
## 2236 -1.015023e-02  5.298989e-02
## 2237  8.597950e-02  8.916929e-02
## 2238  2.367397e-02  9.377095e-02
## 2239  4.250327e-03  1.198558e-01
## 2240 -5.325099e-03  8.480936e-02
## 2241 -2.096233e-02  6.910305e-02
## 2242 -1.243998e-02  7.159861e-02
## 2243  1.791187e-02  1.003685e-01
## 2244 -2.884425e-02  4.723687e-02
## 2245 -2.913409e-02  4.938684e-02
## 2246  3.156780e-01 -2.031192e-01
## 2247 -2.879012e-02  4.710814e-02
## 2248  3.390407e-03  1.264725e-01
## 2249 -2.004604e-02  6.868793e-02
## 2250 -1.588086e-02  6.973558e-02
## 2251 -2.694759e-02  6.187021e-02
## 2252 -1.862087e-02  5.471494e-02
## 2253 -2.942536e-02  7.547327e-02
## 2254 -1.016503e-01 -4.640957e-02
## 2255 -2.403527e-02  5.514086e-02
## 2256 -1.548176e-02  7.672292e-02
## 2257  3.322196e-01 -1.684918e-01
## 2258 -7.116857e-03  1.250715e-01
## 2259 -1.329058e-02  7.900019e-02
## 2260 -2.304467e-03  1.146564e-01
## 2261 -9.165971e-03  8.257596e-02
## 2262 -1.448514e-02  8.263664e-02
## 2263  3.038833e-01 -1.447207e-01
## 2264 -2.157954e-02  8.010489e-02
## 2265 -5.953854e-03  1.112161e-01
## 2266 -7.436679e-03  1.034209e-01
## 2267 -4.660214e-02  2.798493e-02
## 2268 -1.718537e-02  6.787230e-02
## 2269 -3.150978e-02  1.051731e-01
## 2270 -1.878440e-02  6.879236e-02
## 2271 -1.844688e-02  6.174129e-02
## 2272  7.658440e-02  9.283279e-02
## 2273  2.116083e-02  4.446814e-02
## 2274 -1.573844e-02  8.115358e-02
## 2275  2.682144e-02  1.268825e-01
## 2276 -2.980414e-02  5.400815e-02
## 2277  3.627110e-02  6.629557e-02
## 2278 -1.441836e-02  7.870004e-02
## 2279  1.748335e-02  1.272216e-01
## 2280  3.484989e-01 -1.696919e-01
## 2281 -1.240944e-01 -9.335290e-02
## 2282  3.374473e-01 -1.705943e-01
## 2283 -6.996518e-03  9.460775e-02
## 2284 -1.185454e-02  7.324412e-02
## 2285 -1.117990e-02  7.450271e-02
## 2286 -4.462316e-02  1.804615e-02
## 2287  4.576449e-01 -2.576385e-01
## 2288  5.389075e-03  8.003008e-02
## 2289 -8.832811e-03  7.184604e-02
## 2290  1.109094e-04  1.028338e-01
## 2291 -2.521295e-02  5.487935e-02
## 2292  3.668892e-01 -1.946628e-01
## 2293 -5.135119e-03  1.040570e-01
## 2294  1.092338e-02  3.734946e-02
## 2295  7.761361e-02  8.232059e-02
## 2296  7.173461e-02  1.091070e-01
## 2297 -8.202773e-02 -2.103107e-02
## 2298  2.970141e-02  8.983895e-02
## 2299 -1.937251e-02  6.229543e-02
## 2300  3.259078e-01 -1.691688e-01
## 2301 -3.272236e-02  3.644730e-02
## 2302 -1.109292e-01 -5.497774e-02
## 2303  5.591998e-03  9.893809e-02
## 2304 -3.607561e-02  4.081181e-02
## 2305  8.936105e-02  7.706428e-02
## 2306  9.782343e-03  1.077110e-01
## 2307 -2.431655e-02  4.287499e-02
## 2308  1.375657e-02  9.921633e-02
## 2309 -1.135593e-01 -6.420669e-02
## 2310 -7.723531e-02 -2.571545e-02
## 2311 -9.479585e-02 -3.815416e-02
## 2312 -3.275644e-02  5.031806e-02
## 2313 -2.056131e-02  5.988811e-02
## 2314 -5.036247e-02  3.572462e-02
## 2315 -2.039913e-02  8.350822e-02
## 2316 -1.732667e-02  6.885107e-02
## 2317 -9.525566e-03  1.133400e-01
## 2318 -1.452269e-02  6.654317e-02
## 2319 -7.261429e-02 -8.336823e-03
## 2320 -8.184596e-02 -3.526492e-03
## 2321 -1.793614e-02  7.291831e-02
## 2322 -1.284395e-02  7.163548e-02
## 2323 -9.504684e-02 -3.792820e-02
## 2324 -1.351215e-02  6.820163e-02
## 2325 -2.320138e-02  7.948251e-02
## 2326 -2.052274e-02  8.241223e-02
## 2327  2.523135e-01 -9.073705e-02
## 2328 -9.092327e-03  5.879989e-02
## 2329 -3.447404e-02  3.745080e-02
## 2330 -1.592514e-02  7.238394e-02
## 2331 -2.576601e-02  4.461678e-02
## 2332 -2.069534e-02  7.886897e-02
## 2333 -1.486036e-02  1.003996e-01
## 2334 -8.201581e-03  7.988482e-02
## 2335 -7.899247e-03  7.861294e-02
## 2336 -1.297778e-02  7.726137e-02
## 2337 -2.001352e-02  6.742956e-02
## 2338 -6.624789e-02  1.825899e-03
## 2339 -7.216722e-03  1.178298e-01
## 2340  1.188727e-01  5.533894e-02
## 2341  1.578716e-02  4.590657e-02
## 2342 -5.303502e-02  7.201219e-03
## 2343  3.652706e-03  6.070858e-02
## 2344 -1.245357e-02  5.934897e-02
## 2345 -1.860842e-02  7.343515e-02
## 2346 -4.569880e-02  2.352473e-02
## 2347  4.670870e-02  1.100305e-01
## 2348  4.329957e-01 -2.475366e-01
## 2349 -1.700360e-02  7.822251e-02
## 2350 -1.109567e-02  1.084105e-01
## 2351 -8.646322e-04  1.060086e-01
## 2352 -6.825278e-03  5.894880e-02
## 2353  3.064207e-02  4.483195e-02
## 2354  3.689004e-01 -2.103507e-01
## 2355 -7.653536e-02 -2.843673e-02
## 2356 -3.147115e-02  7.262969e-02
## 2357  1.611390e-01  4.204964e-02
## 2358 -2.793665e-02  5.317835e-02
## 2359 -5.424742e-03  1.130490e-01
## 2360  4.707475e-04  4.846898e-02
## 2361  6.465663e-02  1.278908e-02
## 2362 -2.629998e-02  6.271015e-02
## 2363  1.285732e-01  6.147946e-02
## 2364 -1.019082e-01 -6.351688e-02
## 2365  4.348791e-02  8.988930e-02
## 2366 -6.625216e-02  1.446016e-04
## 2367 -1.586065e-02  8.473992e-02
## 2368 -6.173361e-02  7.716942e-03
## 2369 -1.631857e-02  7.833058e-02
## 2370  5.600198e-02  7.432192e-02
## 2371 -9.484289e-03  6.614548e-02
## 2372  4.216567e-01 -2.354409e-01
## 2373 -1.943241e-02  7.577628e-02
## 2374 -1.924445e-02  5.674765e-02
## 2375 -4.663810e-02  1.574203e-02
## 2376 -7.699053e-02 -2.228507e-02
## 2377 -1.819655e-02  7.511733e-02
## 2378 -2.230489e-02  5.839258e-02
## 2379 -6.903617e-02  4.736630e-03
## 2380 -2.544123e-02  8.316254e-02
## 2381  7.693832e-03  9.989054e-02
## 2382  6.377498e-02 -4.427342e-03
## 2383 -1.855582e-02  6.504849e-02
## 2384 -1.972411e-02  6.330393e-02
## 2385 -4.074421e-03  5.511486e-02
## 2386 -2.287437e-02  5.670420e-02
## 2387 -6.959364e-03  1.041048e-01
## 2388 -1.535269e-02  6.828032e-02
## 2389 -1.759696e-02  6.292212e-02
## 2390 -9.664264e-02 -5.313514e-02
## 2391 -4.945613e-02  1.775596e-02
## 2392  1.576130e-03  8.708248e-02
## 2393 -1.938359e-02  5.643768e-02
## 2394 -8.815839e-02 -3.349115e-02
## 2395 -6.482025e-03  1.099643e-01
## 2396  2.829235e-01 -1.280775e-01
## 2397 -1.363603e-02  7.793401e-02
## 2398 -1.026644e-02  6.867268e-02
## 2399  2.879114e-02  8.257634e-02
## 2400 -1.520215e-02  5.792473e-02
## 2401 -3.730714e-02  5.730840e-02
## 2402 -2.265991e-02  9.217766e-02
## 2403 -2.849321e-02  7.327958e-02
## 2404 -2.734178e-02  7.111550e-02
## 2405 -6.123410e-02 -6.539145e-03
## 2406 -1.991812e-02  7.837283e-02
## 2407 -8.003617e-03  7.811188e-02
## 2408 -1.645330e-02  7.209976e-02
## 2409 -2.221229e-02  7.666790e-02
## 2410  3.585157e-02  9.650719e-02
## 2411 -2.622410e-02  9.214843e-02
## 2412  1.718763e-02  8.042434e-02
## 2413 -2.042072e-02  6.529392e-02
## 2414 -2.763495e-02  6.202781e-02
## 2415 -2.574823e-02  5.345225e-02
## 2416  3.073702e-02  5.825670e-02
## 2417  2.445267e-02  5.579517e-02
## 2418 -4.527369e-02  2.194408e-02
## 2419 -1.064637e-02  8.337897e-02
## 2420 -1.727067e-02  6.335840e-02
## 2421  2.632080e-01 -5.714823e-02
## 2422  1.981805e-02  9.523663e-02
## 2423  6.082865e-02  9.228920e-02
## 2424 -3.803448e-03  9.406398e-02
## 2425 -1.487916e-02  8.968482e-02
## 2426 -2.598837e-02  8.186795e-02
## 2427 -4.987599e-02  1.343095e-02
## 2428 -3.981325e-02  4.435177e-02
## 2429 -4.635434e-03  1.156539e-01
## 2430 -1.209406e-03  1.002339e-01
## 2431 -1.838512e-02  6.179891e-02
## 2432 -3.336011e-02  4.505273e-02
## 2433 -1.581389e-02  8.211473e-02
## 2434 -2.526648e-02  5.629026e-02
## 2435 -4.139327e-02  3.454505e-02
## 2436 -6.467876e-02  1.188101e-02
## 2437 -2.503806e-02  5.251851e-02
## 2438 -1.628077e-02  7.008115e-02
## 2439 -2.549323e-02  7.172259e-02
## 2440 -2.047513e-02  8.088962e-02
## 2441 -1.487052e-02  6.140137e-02
## 2442 -1.623775e-02  7.502970e-02
## 2443 -1.289892e-02  6.796841e-02
## 2444 -4.657283e-02  3.231595e-02
## 2445 -1.120981e-02  7.815495e-02
## 2446 -1.629105e-02  1.040089e-01
## 2447  6.827174e-02  5.574199e-02
## 2448 -2.574694e-02  9.624403e-02
## 2449  9.313490e-02 -7.817911e-03
## 2450 -1.851017e-02  8.405967e-02
## 2451 -2.243440e-02  8.577223e-02
## 2452 -1.219702e-02  7.899308e-02
## 2453  2.768572e-03  9.585647e-02
## 2454 -1.574146e-02  6.965401e-02
## 2455 -1.675299e-02  6.800297e-02
## 2456 -5.798962e-02  4.876540e-03
## 2457 -1.531170e-02  8.933803e-02
## 2458 -1.064660e-02  1.042287e-01
## 2459 -2.073037e-02  7.837039e-02
## 2460 -1.676890e-02  7.938343e-02
## 2461 -1.502124e-02  7.427676e-02
## 2462 -2.015270e-02  8.417109e-02
## 2463  1.083298e-01 -3.226056e-02
## 2464 -8.619471e-03  9.237059e-02
## 2465 -7.881898e-03  8.699506e-02
## 2466  2.639405e-02  9.418103e-02
## 2467 -4.171353e-03  1.007600e-01
## 2468 -1.655044e-02  8.050252e-02
## 2469 -7.085083e-03  1.000414e-01
## 2470 -5.140291e-03  1.265121e-01
## 2471 -9.041507e-03  9.916057e-02
## 2472 -1.238675e-02  7.383900e-02
## 2473 -9.582155e-03  8.167937e-02
## 2474 -2.775157e-02  5.127376e-02
## 2475 -5.680922e-02  1.867635e-02
## 2476  4.280701e-02  7.064292e-02
## 2477 -4.182308e-02  2.829416e-02
## 2478 -2.168655e-02  8.545504e-02
## 2479 -2.373238e-02  5.583480e-02
## 2480 -2.026077e-02  7.342854e-02
## 2481 -1.393137e-02  8.655232e-02
## 2482  6.591501e-02 -2.781777e-03
## 2483  6.472674e-03  5.479503e-02
## 2484 -1.632709e-02  8.219724e-02
## 2485 -4.533967e-02  2.641904e-02
## 2486  3.656364e-01 -2.133791e-01
## 2487  1.640167e-03  1.205799e-01
## 2488 -1.796167e-02  6.768103e-02
## 2489 -1.579649e-02  7.603015e-02
## 2490 -1.942150e-02  8.266531e-02
## 2491 -3.334929e-02  5.537176e-02
## 2492 -4.607470e-02  3.766712e-02
## 2493 -1.547251e-02  5.971184e-02
## 2494 -1.097564e-02  9.498544e-02
## 2495  2.381106e-01 -1.319978e-01
## 2496 -2.092947e-02  5.055958e-02
## 2497 -1.183829e-02  9.178250e-02
## 2498  1.282811e-03  1.249058e-01
## 2499 -1.438649e-02  7.849231e-02
## 2500 -1.729831e-02  7.863045e-02
## 2501  4.126233e-01 -2.378462e-01
## 2502 -1.585440e-02  7.141747e-02
## 2503 -1.935360e-02  7.280192e-02
## 2504 -2.946424e-02  5.684346e-02
## 2505 -5.009570e-02  2.536252e-02
## 2506 -3.740001e-02  2.775089e-02
## 2507 -1.018800e-02  8.798213e-02
## 2508 -1.795318e-02  6.204994e-02
## 2509 -3.616703e-02  3.700281e-02
## 2510 -2.310908e-02  5.235364e-02
## 2511  6.350948e-02  2.692692e-02
## 2512  4.371299e-01 -2.293118e-01
## 2513 -3.123598e-02  4.571339e-02
## 2514 -2.064692e-02  6.059117e-02
## 2515  9.275567e-02  6.557015e-02
## 2516 -7.983072e-03  1.066657e-01
## 2517 -2.962494e-02  5.579835e-02
## 2518 -1.385806e-03  1.001482e-01
## 2519 -4.743294e-02  4.016855e-02
## 2520 -1.984021e-02  6.153641e-02
## 2521  2.773875e-01 -1.543300e-01
## 2522 -8.464425e-03  8.955463e-02
## 2523 -1.845990e-02  6.911239e-02
## 2524 -1.530341e-02  6.252380e-02
## 2525 -4.034339e-02  4.190072e-02
## 2526 -1.727236e-02  6.195449e-02
## 2527 -2.010199e-02  8.093499e-02
## 2528 -1.719088e-02  7.967719e-02
## 2529 -1.159320e-02  6.750458e-02
## 2530 -2.079465e-02  4.428524e-02
## 2531 -1.175759e-02  9.117831e-02
## 2532 -2.527496e-02  8.084169e-02
## 2533 -2.317550e-02  7.712938e-02
## 2534 -1.244964e-02  8.083009e-02
## 2535 -2.113476e-02  6.806426e-02
## 2536 -1.609317e-02  6.573818e-02
## 2537 -1.938465e-02  6.630264e-02
## 2538 -2.719651e-02  7.062746e-02
## 2539 -2.219018e-02  8.607320e-02
## 2540 -1.975717e-02  7.820925e-02
## 2541 -2.450161e-02  5.576512e-02
## 2542 -6.337605e-02  1.160553e-03
## 2543 -5.348834e-02  2.827797e-02
## 2544 -1.661335e-02  6.838721e-02
## 2545 -2.230698e-02  8.473215e-02
## 2546 -2.063906e-02  6.515709e-02
## 2547 -2.415695e-02  8.594143e-02
## 2548  7.759426e-02  1.091927e-01
## 2549 -4.935317e-02  2.533802e-02
## 2550  1.096768e-01  4.006466e-02
## 2551 -1.499001e-02  7.574651e-02
## 2552 -7.450466e-03  9.349618e-02
## 2553 -8.785823e-02 -3.604009e-02
## 2554 -1.412831e-02  6.862862e-02
## 2555 -8.555333e-02 -3.570022e-02
## 2556  1.251924e-01  4.916965e-02
## 2557 -7.555866e-03  8.826105e-02
## 2558 -1.718793e-02  7.701536e-02
## 2559  1.405605e-02  8.665068e-02
## 2560 -9.711831e-02 -4.796720e-02
## 2561  1.411223e-02  9.259760e-02
## 2562 -1.955081e-02  6.062116e-02
## 2563 -2.067964e-02  5.776991e-02
## 2564 -2.126780e-02  8.465822e-02
## 2565 -1.552526e-02  6.688461e-02
## 2566 -1.085018e-02  1.061512e-01
## 2567 -1.786359e-02  8.468595e-02
## 2568  1.067328e-01 -3.772517e-02
## 2569  1.722367e-02  1.234825e-01
## 2570 -1.577335e-02  6.159030e-02
## 2571 -1.962573e-02  7.879311e-02
## 2572 -2.189885e-02  8.922390e-02
## 2573 -1.755985e-02  5.292921e-02
## 2574 -5.854045e-03  1.006507e-01
## 2575 -2.958497e-02  4.668073e-02
## 2576 -2.965545e-03  6.905526e-02
## 2577 -2.264625e-02  4.964055e-02
## 2578 -1.505236e-02  7.642707e-02
## 2579 -2.019674e-02  6.256269e-02
## 2580 -4.236228e-02  5.267864e-02
## 2581 -1.955458e-02  7.850382e-02
## 2582 -1.445063e-02  6.696492e-02
## 2583 -1.437731e-02  1.047265e-01
## 2584 -2.192619e-02  8.474992e-02
## 2585 -6.689631e-02  6.514266e-04
## 2586 -2.389058e-02  9.165086e-02
## 2587 -1.858873e-02  6.718348e-02
## 2588 -5.043752e-02  2.804625e-02
## 2589 -1.834313e-02  5.120517e-02
## 2590 -1.604436e-02  4.927768e-02
## 2591  1.618302e-03  8.713669e-02
## 2592 -3.395193e-03  4.631729e-02
## 2593 -1.900246e-02  8.025419e-02
## 2594 -1.381187e-02  5.888477e-02
## 2595 -3.908057e-02  2.842485e-02
## 2596 -3.238748e-02  3.724718e-02
## 2597 -1.508061e-02  6.648695e-02
## 2598  5.095909e-02  1.654924e-02
## 2599  9.242236e-02 -1.847173e-02
## 2600  2.770943e-01 -1.492224e-01
## 2601 -1.717151e-02  7.468197e-02
## 2602 -2.098874e-02  6.728764e-02
## 2603 -1.593195e-02  6.090587e-02
## 2604 -8.561677e-02 -2.186658e-02
## 2605 -1.802056e-02  6.319759e-02
## 2606 -1.679599e-02  6.215302e-02
## 2607 -1.930211e-02  6.940245e-02
## 2608 -1.867820e-02  5.696320e-02
## 2609  1.138513e-01 -2.260896e-02
## 2610 -6.844480e-02  8.946632e-04
## 2611 -3.354762e-02  4.285667e-02
## 2612 -1.388750e-02  8.409088e-02
## 2613 -1.939786e-02  8.032807e-02
## 2614  6.181376e-02  3.411990e-02
## 2615 -1.120751e-02  8.121276e-02
## 2616 -1.942308e-02  6.262819e-02
## 2617 -1.545495e-02  8.563498e-02
## 2618 -1.653496e-02  6.546095e-02
## 2619 -1.680460e-02  7.187519e-02
## 2620 -1.911355e-02  7.459205e-02
## 2621  1.179503e-01 -1.416050e-02
## 2622 -1.765192e-02  6.783616e-02
## 2623 -1.709269e-02  7.006766e-02
## 2624 -8.879349e-03  9.583573e-02
## 2625 -1.358159e-02  7.504940e-02
## 2626 -2.146967e-02  6.296901e-02
## 2627 -1.907694e-02  6.529193e-02
## 2628 -1.868409e-02  7.122325e-02
## 2629 -1.659893e-02  5.559075e-02
## 2630 -1.440008e-03  1.003651e-01
## 2631  9.559150e-02  3.718538e-02
## 2632 -2.434837e-02  8.609689e-02
## 2633  1.057849e-01  1.145238e-02
## 2634 -1.812374e-02  6.527840e-02
## 2635 -1.301225e-02  8.810173e-02
## 2636 -1.775780e-02  8.421322e-02
## 2637 -3.448333e-02  3.560492e-02
## 2638  1.155216e-03  1.157451e-01
## 2639 -1.646654e-02  6.044369e-02
## 2640 -1.373327e-02  6.389098e-02
## 2641  5.529506e-02  8.608505e-02
## 2642 -2.327355e-02  7.177264e-02
## 2643  3.678880e-03  5.038934e-02
## 2644 -2.321592e-02  4.936674e-02
## 2645 -8.558403e-03  6.614254e-02
## 2646 -1.459996e-02  6.487822e-02
## 2647 -4.829039e-02  2.067245e-02
## 2648 -1.646608e-02  6.134384e-02
## 2649 -1.168769e-02  6.489649e-02
## 2650  1.576058e-01 -2.270635e-03
## 2651 -2.508836e-02  4.776582e-02
## 2652  1.609746e-02  9.141033e-02
## 2653 -1.825139e-02  6.297245e-02
## 2654 -1.827147e-02  5.673931e-02
## 2655 -2.116849e-02  6.979609e-02
## 2656 -1.545702e-02  7.142045e-02
## 2657 -6.504343e-02  5.376839e-03
## 2658 -1.786944e-02  6.279207e-02
## 2659  1.786264e-01 -6.418291e-02
## 2660 -1.539834e-02  6.646250e-02
## 2661 -2.524136e-02  6.731802e-02
## 2662 -1.444465e-02  7.370711e-02
## 2663 -1.568547e-02  5.567660e-02
## 2664 -1.406381e-02  6.583635e-02
## 2665 -1.609973e-02  6.593410e-02
## 2666  1.416843e-02  8.373666e-02
## 2667 -6.665105e-03  8.216067e-02
## 
## $eig
##    [1]  3.947033e+01  2.785304e+01  2.113279e+01  1.660350e+01
##    [5]  1.514453e+01  1.369256e+01  1.236608e+01  1.200663e+01
##    [9]  1.086000e+01  1.054257e+01  1.040641e+01  9.896183e+00
##   [13]  9.447034e+00  8.845980e+00  8.400266e+00  8.129663e+00
##   [17]  7.846819e+00  7.735503e+00  7.560077e+00  6.881846e+00
##   [21]  6.724321e+00  6.505764e+00  6.455684e+00  6.229806e+00
##   [25]  6.087511e+00  6.024707e+00  5.891958e+00  5.773067e+00
##   [29]  5.705472e+00  5.545264e+00  5.481257e+00  5.337754e+00
##   [33]  5.295979e+00  5.223134e+00  5.135956e+00  5.052003e+00
##   [37]  4.985266e+00  4.918590e+00  4.861479e+00  4.811359e+00
##   [41]  4.725782e+00  4.713585e+00  4.663872e+00  4.571594e+00
##   [45]  4.517310e+00  4.485587e+00  4.404087e+00  4.383477e+00
##   [49]  4.281294e+00  4.245951e+00  4.197766e+00  4.100257e+00
##   [53]  4.097683e+00  4.038224e+00  4.010094e+00  3.979057e+00
##   [57]  3.953718e+00  3.918488e+00  3.865423e+00  3.842007e+00
##   [61]  3.781778e+00  3.749657e+00  3.727488e+00  3.715595e+00
##   [65]  3.694624e+00  3.646652e+00  3.605212e+00  3.588211e+00
##   [69]  3.557018e+00  3.539244e+00  3.516298e+00  3.480705e+00
##   [73]  3.463639e+00  3.435639e+00  3.414019e+00  3.394163e+00
##   [77]  3.377407e+00  3.363110e+00  3.333023e+00  3.312725e+00
##   [81]  3.298132e+00  3.283461e+00  3.253585e+00  3.238265e+00
##   [85]  3.227048e+00  3.211254e+00  3.197394e+00  3.178268e+00
##   [89]  3.157654e+00  3.149127e+00  3.143928e+00  3.120566e+00
##   [93]  3.103262e+00  3.079943e+00  3.064265e+00  3.048501e+00
##   [97]  3.033122e+00  3.012494e+00  3.009934e+00  2.995965e+00
##  [101]  2.976853e+00  2.959604e+00  2.946298e+00  2.932501e+00
##  [105]  2.907423e+00  2.903891e+00  2.892829e+00  2.881053e+00
##  [109]  2.867748e+00  2.856755e+00  2.835590e+00  2.832441e+00
##  [113]  2.826991e+00  2.820342e+00  2.793801e+00  2.791216e+00
##  [117]  2.780021e+00  2.761109e+00  2.756851e+00  2.750793e+00
##  [121]  2.729070e+00  2.724937e+00  2.709325e+00  2.702045e+00
##  [125]  2.694880e+00  2.680996e+00  2.668761e+00  2.653505e+00
##  [129]  2.651023e+00  2.643463e+00  2.638347e+00  2.626930e+00
##  [133]  2.624792e+00  2.607071e+00  2.601529e+00  2.586097e+00
##  [137]  2.584415e+00  2.579095e+00  2.568976e+00  2.559095e+00
##  [141]  2.553750e+00  2.544389e+00  2.530808e+00  2.527547e+00
##  [145]  2.519760e+00  2.500972e+00  2.496668e+00  2.486945e+00
##  [149]  2.481880e+00  2.470168e+00  2.467518e+00  2.460720e+00
##  [153]  2.445496e+00  2.432701e+00  2.427539e+00  2.418019e+00
##  [157]  2.415739e+00  2.406809e+00  2.405231e+00  2.398677e+00
##  [161]  2.391633e+00  2.381092e+00  2.377904e+00  2.367492e+00
##  [165]  2.360841e+00  2.357742e+00  2.344557e+00  2.337019e+00
##  [169]  2.332420e+00  2.329121e+00  2.320611e+00  2.317781e+00
##  [173]  2.308191e+00  2.297816e+00  2.295692e+00  2.287247e+00
##  [177]  2.281822e+00  2.278233e+00  2.270881e+00  2.263566e+00
##  [181]  2.258764e+00  2.251266e+00  2.249296e+00  2.239760e+00
##  [185]  2.237010e+00  2.232133e+00  2.225193e+00  2.223398e+00
##  [189]  2.218074e+00  2.207894e+00  2.204665e+00  2.196574e+00
##  [193]  2.194958e+00  2.184993e+00  2.181569e+00  2.177559e+00
##  [197]  2.172402e+00  2.163906e+00  2.159550e+00  2.156195e+00
##  [201]  2.151170e+00  2.145963e+00  2.144030e+00  2.135409e+00
##  [205]  2.129365e+00  2.126321e+00  2.116270e+00  2.112545e+00
##  [209]  2.108550e+00  2.105241e+00  2.093301e+00  2.088861e+00
##  [213]  2.079760e+00  2.076305e+00  2.071209e+00  2.069877e+00
##  [217]  2.059898e+00  2.056446e+00  2.055802e+00  2.051879e+00
##  [221]  2.046017e+00  2.041215e+00  2.036132e+00  2.031628e+00
##  [225]  2.026240e+00  2.018754e+00  2.015952e+00  2.008679e+00
##  [229]  2.007374e+00  2.002453e+00  2.000914e+00  1.994058e+00
##  [233]  1.988446e+00  1.985601e+00  1.979495e+00  1.977317e+00
##  [237]  1.971740e+00  1.965614e+00  1.963217e+00  1.954265e+00
##  [241]  1.951471e+00  1.946011e+00  1.942204e+00  1.941587e+00
##  [245]  1.935370e+00  1.932993e+00  1.928598e+00  1.923142e+00
##  [249]  1.917690e+00  1.913623e+00  1.911162e+00  1.905311e+00
##  [253]  1.902506e+00  1.893735e+00  1.890561e+00  1.883801e+00
##  [257]  1.883035e+00  1.881639e+00  1.876563e+00  1.874248e+00
##  [261]  1.869767e+00  1.861788e+00  1.857630e+00  1.851448e+00
##  [265]  1.846106e+00  1.845093e+00  1.842416e+00  1.834325e+00
##  [269]  1.833182e+00  1.828979e+00  1.824026e+00  1.822527e+00
##  [273]  1.813647e+00  1.810844e+00  1.806880e+00  1.802880e+00
##  [277]  1.800927e+00  1.797693e+00  1.790182e+00  1.789162e+00
##  [281]  1.784892e+00  1.780304e+00  1.777216e+00  1.773885e+00
##  [285]  1.770740e+00  1.766170e+00  1.762172e+00  1.760480e+00
##  [289]  1.758333e+00  1.755212e+00  1.752368e+00  1.746861e+00
##  [293]  1.740200e+00  1.738129e+00  1.736073e+00  1.733717e+00
##  [297]  1.729613e+00  1.725966e+00  1.721026e+00  1.718204e+00
##  [301]  1.717526e+00  1.715272e+00  1.708763e+00  1.706419e+00
##  [305]  1.704770e+00  1.703184e+00  1.699244e+00  1.698272e+00
##  [309]  1.691916e+00  1.686964e+00  1.686240e+00  1.682781e+00
##  [313]  1.680072e+00  1.677018e+00  1.673800e+00  1.669652e+00
##  [317]  1.666708e+00  1.659857e+00  1.657845e+00  1.655842e+00
##  [321]  1.652831e+00  1.648163e+00  1.647293e+00  1.645415e+00
##  [325]  1.643300e+00  1.639334e+00  1.635445e+00  1.629095e+00
##  [329]  1.626916e+00  1.624668e+00  1.623990e+00  1.617121e+00
##  [333]  1.615903e+00  1.613219e+00  1.610599e+00  1.607679e+00
##  [337]  1.604730e+00  1.601253e+00  1.598995e+00  1.597645e+00
##  [341]  1.591808e+00  1.589047e+00  1.586866e+00  1.579462e+00
##  [345]  1.578567e+00  1.577678e+00  1.569722e+00  1.568689e+00
##  [349]  1.567292e+00  1.566343e+00  1.562158e+00  1.559632e+00
##  [353]  1.554291e+00  1.551818e+00  1.550368e+00  1.547893e+00
##  [357]  1.544694e+00  1.543137e+00  1.539882e+00  1.538190e+00
##  [361]  1.535749e+00  1.533301e+00  1.532205e+00  1.528349e+00
##  [365]  1.525753e+00  1.522876e+00  1.519802e+00  1.516759e+00
##  [369]  1.515838e+00  1.513491e+00  1.511347e+00  1.508333e+00
##  [373]  1.505749e+00  1.504908e+00  1.499436e+00  1.496475e+00
##  [377]  1.494594e+00  1.493210e+00  1.489411e+00  1.488408e+00
##  [381]  1.486904e+00  1.484084e+00  1.478474e+00  1.478146e+00
##  [385]  1.475174e+00  1.474231e+00  1.471398e+00  1.464607e+00
##  [389]  1.462403e+00  1.459625e+00  1.455346e+00  1.452661e+00
##  [393]  1.450992e+00  1.449214e+00  1.445842e+00  1.443497e+00
##  [397]  1.440489e+00  1.436463e+00  1.435128e+00  1.432640e+00
##  [401]  1.431388e+00  1.427231e+00  1.426152e+00  1.423319e+00
##  [405]  1.422763e+00  1.420288e+00  1.417269e+00  1.415589e+00
##  [409]  1.412448e+00  1.410197e+00  1.409479e+00  1.406273e+00
##  [413]  1.403695e+00  1.402680e+00  1.400512e+00  1.399136e+00
##  [417]  1.391777e+00  1.389759e+00  1.388599e+00  1.386385e+00
##  [421]  1.385928e+00  1.383056e+00  1.381110e+00  1.376345e+00
##  [425]  1.375191e+00  1.373573e+00  1.369148e+00  1.367233e+00
##  [429]  1.366882e+00  1.364316e+00  1.363782e+00  1.361676e+00
##  [433]  1.358630e+00  1.357537e+00  1.352385e+00  1.349290e+00
##  [437]  1.348129e+00  1.344479e+00  1.340790e+00  1.340035e+00
##  [441]  1.336224e+00  1.335515e+00  1.332756e+00  1.330139e+00
##  [445]  1.326888e+00  1.325588e+00  1.324283e+00  1.321186e+00
##  [449]  1.320278e+00  1.318906e+00  1.317654e+00  1.315455e+00
##  [453]  1.311800e+00  1.310025e+00  1.307052e+00  1.306299e+00
##  [457]  1.303514e+00  1.299617e+00  1.298911e+00  1.297191e+00
##  [461]  1.296292e+00  1.293093e+00  1.291887e+00  1.288380e+00
##  [465]  1.286784e+00  1.284251e+00  1.281423e+00  1.280318e+00
##  [469]  1.278162e+00  1.277519e+00  1.274558e+00  1.271320e+00
##  [473]  1.269952e+00  1.266856e+00  1.264739e+00  1.261408e+00
##  [477]  1.259124e+00  1.257153e+00  1.254698e+00  1.252989e+00
##  [481]  1.252183e+00  1.251011e+00  1.248589e+00  1.246172e+00
##  [485]  1.244897e+00  1.241375e+00  1.239469e+00  1.237513e+00
##  [489]  1.236939e+00  1.233720e+00  1.231685e+00  1.228352e+00
##  [493]  1.225949e+00  1.225433e+00  1.224258e+00  1.222120e+00
##  [497]  1.220021e+00  1.217195e+00  1.216133e+00  1.213826e+00
##  [501]  1.210333e+00  1.209809e+00  1.206346e+00  1.204815e+00
##  [505]  1.201867e+00  1.200358e+00  1.198107e+00  1.196082e+00
##  [509]  1.194291e+00  1.192639e+00  1.192314e+00  1.189038e+00
##  [513]  1.188184e+00  1.183275e+00  1.182320e+00  1.181578e+00
##  [517]  1.180143e+00  1.177113e+00  1.175575e+00  1.174035e+00
##  [521]  1.170142e+00  1.168709e+00  1.166643e+00  1.166275e+00
##  [525]  1.162857e+00  1.162155e+00  1.159322e+00  1.157650e+00
##  [529]  1.155707e+00  1.153209e+00  1.151140e+00  1.150083e+00
##  [533]  1.148128e+00  1.146207e+00  1.144857e+00  1.143035e+00
##  [537]  1.141526e+00  1.140839e+00  1.138408e+00  1.133981e+00
##  [541]  1.132916e+00  1.131766e+00  1.130492e+00  1.127197e+00
##  [545]  1.126263e+00  1.124339e+00  1.122769e+00  1.121725e+00
##  [549]  1.120389e+00  1.117053e+00  1.115118e+00  1.113445e+00
##  [553]  1.111528e+00  1.108896e+00  1.107270e+00  1.103244e+00
##  [557]  1.101760e+00  1.100627e+00  1.099886e+00  1.096475e+00
##  [561]  1.094684e+00  1.094547e+00  1.090673e+00  1.089396e+00
##  [565]  1.086712e+00  1.085907e+00  1.084032e+00  1.083072e+00
##  [569]  1.082473e+00  1.081585e+00  1.080016e+00  1.078394e+00
##  [573]  1.075247e+00  1.074480e+00  1.072234e+00  1.070344e+00
##  [577]  1.067400e+00  1.065482e+00  1.064463e+00  1.061542e+00
##  [581]  1.059559e+00  1.057564e+00  1.055537e+00  1.054158e+00
##  [585]  1.053691e+00  1.051010e+00  1.050275e+00  1.049233e+00
##  [589]  1.047610e+00  1.046393e+00  1.043513e+00  1.040224e+00
##  [593]  1.039712e+00  1.038679e+00  1.035729e+00  1.034790e+00
##  [597]  1.029781e+00  1.029722e+00  1.027936e+00  1.026657e+00
##  [601]  1.023562e+00  1.022851e+00  1.021326e+00  1.020373e+00
##  [605]  1.017894e+00  1.015347e+00  1.014776e+00  1.012384e+00
##  [609]  1.011664e+00  1.009223e+00  1.008332e+00  1.006826e+00
##  [613]  1.004777e+00  1.003975e+00  1.002618e+00  9.999259e-01
##  [617]  9.987813e-01  9.968799e-01  9.957302e-01  9.917991e-01
##  [621]  9.901167e-01  9.897286e-01  9.872210e-01  9.862628e-01
##  [625]  9.825057e-01  9.820324e-01  9.802057e-01  9.793369e-01
##  [629]  9.781229e-01  9.766386e-01  9.749331e-01  9.739341e-01
##  [633]  9.714515e-01  9.703024e-01  9.697488e-01  9.683494e-01
##  [637]  9.658512e-01  9.639937e-01  9.608097e-01  9.593784e-01
##  [641]  9.584685e-01  9.580188e-01  9.559774e-01  9.540550e-01
##  [645]  9.527857e-01  9.512933e-01  9.493536e-01  9.484200e-01
##  [649]  9.460498e-01  9.451505e-01  9.438293e-01  9.426074e-01
##  [653]  9.407966e-01  9.400523e-01  9.384903e-01  9.370296e-01
##  [657]  9.360037e-01  9.341242e-01  9.326513e-01  9.304286e-01
##  [661]  9.277458e-01  9.274048e-01  9.238026e-01  9.229483e-01
##  [665]  9.215412e-01  9.210678e-01  9.182045e-01  9.165558e-01
##  [669]  9.140576e-01  9.132067e-01  9.119856e-01  9.103989e-01
##  [673]  9.079915e-01  9.069984e-01  9.064688e-01  9.047207e-01
##  [677]  9.036349e-01  9.011058e-01  8.999375e-01  8.991841e-01
##  [681]  8.985403e-01  8.952570e-01  8.948372e-01  8.943757e-01
##  [685]  8.927746e-01  8.916560e-01  8.888490e-01  8.869541e-01
##  [689]  8.864432e-01  8.845835e-01  8.827148e-01  8.818280e-01
##  [693]  8.815872e-01  8.790335e-01  8.779287e-01  8.764466e-01
##  [697]  8.745321e-01  8.738508e-01  8.726316e-01  8.715487e-01
##  [701]  8.700065e-01  8.675551e-01  8.659733e-01  8.647259e-01
##  [705]  8.633305e-01  8.626869e-01  8.615206e-01  8.598209e-01
##  [709]  8.578720e-01  8.561242e-01  8.539504e-01  8.534969e-01
##  [713]  8.519533e-01  8.504954e-01  8.491278e-01  8.477737e-01
##  [717]  8.464346e-01  8.434497e-01  8.416888e-01  8.409214e-01
##  [721]  8.400039e-01  8.393817e-01  8.381558e-01  8.366280e-01
##  [725]  8.337236e-01  8.320302e-01  8.311338e-01  8.280660e-01
##  [729]  8.263588e-01  8.251898e-01  8.240955e-01  8.236308e-01
##  [733]  8.226645e-01  8.211768e-01  8.178544e-01  8.169510e-01
##  [737]  8.153465e-01  8.148931e-01  8.134196e-01  8.115666e-01
##  [741]  8.103508e-01  8.099846e-01  8.063535e-01  8.054483e-01
##  [745]  8.042333e-01  8.029305e-01  8.018040e-01  7.995917e-01
##  [749]  7.992193e-01  7.977856e-01  7.966862e-01  7.953189e-01
##  [753]  7.934143e-01  7.928580e-01  7.908221e-01  7.898351e-01
##  [757]  7.887053e-01  7.856233e-01  7.846295e-01  7.840404e-01
##  [761]  7.817752e-01  7.811906e-01  7.800800e-01  7.787566e-01
##  [765]  7.765360e-01  7.742018e-01  7.738785e-01  7.728172e-01
##  [769]  7.724427e-01  7.696701e-01  7.693155e-01  7.688748e-01
##  [773]  7.657740e-01  7.649060e-01  7.634922e-01  7.613010e-01
##  [777]  7.601040e-01  7.596750e-01  7.579714e-01  7.566998e-01
##  [781]  7.559059e-01  7.550283e-01  7.529420e-01  7.519746e-01
##  [785]  7.503234e-01  7.491111e-01  7.484362e-01  7.478946e-01
##  [789]  7.448258e-01  7.428153e-01  7.419537e-01  7.412485e-01
##  [793]  7.402815e-01  7.382733e-01  7.364311e-01  7.360464e-01
##  [797]  7.355399e-01  7.337238e-01  7.322274e-01  7.311394e-01
##  [801]  7.295625e-01  7.281417e-01  7.270037e-01  7.261064e-01
##  [805]  7.251767e-01  7.228049e-01  7.216090e-01  7.186537e-01
##  [809]  7.168330e-01  7.156283e-01  7.151021e-01  7.148100e-01
##  [813]  7.122051e-01  7.119006e-01  7.115209e-01  7.105115e-01
##  [817]  7.090241e-01  7.073926e-01  7.061828e-01  7.039466e-01
##  [821]  7.015582e-01  7.014086e-01  7.005634e-01  6.991164e-01
##  [825]  6.989157e-01  6.966456e-01  6.949728e-01  6.937635e-01
##  [829]  6.924408e-01  6.906985e-01  6.899475e-01  6.889181e-01
##  [833]  6.867509e-01  6.852423e-01  6.850800e-01  6.825828e-01
##  [837]  6.819775e-01  6.804916e-01  6.799175e-01  6.791574e-01
##  [841]  6.777446e-01  6.770536e-01  6.760499e-01  6.750627e-01
##  [845]  6.732833e-01  6.724078e-01  6.702238e-01  6.693071e-01
##  [849]  6.684241e-01  6.665745e-01  6.657617e-01  6.636964e-01
##  [853]  6.621298e-01  6.617168e-01  6.606240e-01  6.577912e-01
##  [857]  6.571522e-01  6.559206e-01  6.551433e-01  6.550014e-01
##  [861]  6.537828e-01  6.511853e-01  6.500290e-01  6.484020e-01
##  [865]  6.476458e-01  6.466438e-01  6.449018e-01  6.444518e-01
##  [869]  6.425072e-01  6.421874e-01  6.406731e-01  6.377274e-01
##  [873]  6.370044e-01  6.364849e-01  6.351853e-01  6.336602e-01
##  [877]  6.322749e-01  6.315117e-01  6.299403e-01  6.280036e-01
##  [881]  6.273840e-01  6.254214e-01  6.246636e-01  6.234717e-01
##  [885]  6.231246e-01  6.207383e-01  6.193749e-01  6.187583e-01
##  [889]  6.181879e-01  6.163142e-01  6.160907e-01  6.132992e-01
##  [893]  6.129799e-01  6.120664e-01  6.103068e-01  6.081683e-01
##  [897]  6.076008e-01  6.065335e-01  6.052248e-01  6.040887e-01
##  [901]  6.031362e-01  6.024467e-01  6.005508e-01  5.989041e-01
##  [905]  5.977424e-01  5.969932e-01  5.964300e-01  5.959364e-01
##  [909]  5.940107e-01  5.930489e-01  5.914963e-01  5.904662e-01
##  [913]  5.890068e-01  5.884260e-01  5.866702e-01  5.853885e-01
##  [917]  5.844671e-01  5.839246e-01  5.828606e-01  5.820296e-01
##  [921]  5.805180e-01  5.793611e-01  5.775217e-01  5.766620e-01
##  [925]  5.759930e-01  5.751084e-01  5.746501e-01  5.738800e-01
##  [929]  5.715671e-01  5.695162e-01  5.687409e-01  5.683021e-01
##  [933]  5.677726e-01  5.669311e-01  5.663721e-01  5.639560e-01
##  [937]  5.623565e-01  5.609365e-01  5.600621e-01  5.592828e-01
##  [941]  5.578410e-01  5.573904e-01  5.556850e-01  5.551474e-01
##  [945]  5.532742e-01  5.520399e-01  5.511679e-01  5.488852e-01
##  [949]  5.476847e-01  5.473717e-01  5.458316e-01  5.446615e-01
##  [953]  5.441563e-01  5.435886e-01  5.415275e-01  5.405465e-01
##  [957]  5.398727e-01  5.368905e-01  5.355189e-01  5.353072e-01
##  [961]  5.336032e-01  5.327353e-01  5.316927e-01  5.307335e-01
##  [965]  5.290603e-01  5.280322e-01  5.268097e-01  5.265487e-01
##  [969]  5.260306e-01  5.241790e-01  5.216547e-01  5.211013e-01
##  [973]  5.198500e-01  5.186009e-01  5.171060e-01  5.162979e-01
##  [977]  5.156400e-01  5.136135e-01  5.129053e-01  5.123865e-01
##  [981]  5.098275e-01  5.080552e-01  5.079169e-01  5.072589e-01
##  [985]  5.067496e-01  5.053514e-01  5.040393e-01  5.026376e-01
##  [989]  5.020114e-01  5.006398e-01  5.001115e-01  4.987563e-01
##  [993]  4.982102e-01  4.967326e-01  4.958024e-01  4.934117e-01
##  [997]  4.930954e-01  4.916019e-01  4.911674e-01  4.907156e-01
## [1001]  4.884762e-01  4.876610e-01  4.858385e-01  4.847837e-01
## [1005]  4.836183e-01  4.821529e-01  4.814787e-01  4.810944e-01
## [1009]  4.786216e-01  4.785324e-01  4.771340e-01  4.768795e-01
## [1013]  4.760599e-01  4.746080e-01  4.734185e-01  4.728035e-01
## [1017]  4.716607e-01  4.700514e-01  4.699620e-01  4.679491e-01
## [1021]  4.670977e-01  4.658159e-01  4.650839e-01  4.639216e-01
## [1025]  4.626309e-01  4.614649e-01  4.599555e-01  4.580473e-01
## [1029]  4.578522e-01  4.573616e-01  4.559068e-01  4.552818e-01
## [1033]  4.536246e-01  4.525103e-01  4.515559e-01  4.504637e-01
## [1037]  4.495925e-01  4.485075e-01  4.474795e-01  4.470856e-01
## [1041]  4.465925e-01  4.451087e-01  4.428205e-01  4.421369e-01
## [1045]  4.405869e-01  4.398207e-01  4.386223e-01  4.385603e-01
## [1049]  4.378761e-01  4.373968e-01  4.366048e-01  4.339595e-01
## [1053]  4.333299e-01  4.323250e-01  4.310325e-01  4.309354e-01
## [1057]  4.285384e-01  4.281538e-01  4.270149e-01  4.261829e-01
## [1061]  4.240780e-01  4.226260e-01  4.223091e-01  4.214849e-01
## [1065]  4.199827e-01  4.188693e-01  4.174357e-01  4.168420e-01
## [1069]  4.162238e-01  4.151349e-01  4.144775e-01  4.134486e-01
## [1073]  4.125060e-01  4.115400e-01  4.096996e-01  4.089139e-01
## [1077]  4.084041e-01  4.080689e-01  4.071886e-01  4.051338e-01
## [1081]  4.049484e-01  4.036197e-01  4.024737e-01  4.021270e-01
## [1085]  4.001544e-01  3.998735e-01  3.988334e-01  3.982991e-01
## [1089]  3.975720e-01  3.952299e-01  3.944607e-01  3.936742e-01
## [1093]  3.924696e-01  3.918639e-01  3.910878e-01  3.905198e-01
## [1097]  3.895910e-01  3.882185e-01  3.879040e-01  3.873318e-01
## [1101]  3.865419e-01  3.844262e-01  3.827971e-01  3.818508e-01
## [1105]  3.815459e-01  3.804263e-01  3.797290e-01  3.778410e-01
## [1109]  3.763453e-01  3.748262e-01  3.745590e-01  3.734005e-01
## [1113]  3.730044e-01  3.713161e-01  3.706886e-01  3.682990e-01
## [1117]  3.673443e-01  3.669380e-01  3.663912e-01  3.660947e-01
## [1121]  3.648264e-01  3.636767e-01  3.624869e-01  3.607406e-01
## [1125]  3.584166e-01  3.579781e-01  3.565952e-01  3.561384e-01
## [1129]  3.550919e-01  3.539661e-01  3.523214e-01  3.516654e-01
## [1133]  3.508689e-01  3.496632e-01  3.491597e-01  3.482726e-01
## [1137]  3.473637e-01  3.456404e-01  3.449096e-01  3.443991e-01
## [1141]  3.427271e-01  3.423989e-01  3.418016e-01  3.396571e-01
## [1145]  3.385978e-01  3.375350e-01  3.365062e-01  3.342021e-01
## [1149]  3.339371e-01  3.335188e-01  3.330342e-01  3.320682e-01
## [1153]  3.310830e-01  3.304675e-01  3.302765e-01  3.289298e-01
## [1157]  3.273264e-01  3.272839e-01  3.262216e-01  3.244009e-01
## [1161]  3.231616e-01  3.220943e-01  3.210609e-01  3.200066e-01
## [1165]  3.188631e-01  3.179760e-01  3.172601e-01  3.169918e-01
## [1169]  3.151198e-01  3.140240e-01  3.138737e-01  3.129145e-01
## [1173]  3.124619e-01  3.113552e-01  3.102795e-01  3.095668e-01
## [1177]  3.089831e-01  3.082999e-01  3.062126e-01  3.054419e-01
## [1181]  3.041615e-01  3.039843e-01  3.033676e-01  3.023430e-01
## [1185]  3.004565e-01  2.983886e-01  2.976040e-01  2.967263e-01
## [1189]  2.964885e-01  2.957875e-01  2.946363e-01  2.937733e-01
## [1193]  2.930404e-01  2.920304e-01  2.912268e-01  2.899059e-01
## [1197]  2.889115e-01  2.881624e-01  2.871528e-01  2.859017e-01
## [1201]  2.856598e-01  2.839238e-01  2.821701e-01  2.816965e-01
## [1205]  2.810901e-01  2.803763e-01  2.800413e-01  2.789734e-01
## [1209]  2.779210e-01  2.766719e-01  2.745169e-01  2.739496e-01
## [1213]  2.737604e-01  2.725707e-01  2.717782e-01  2.710730e-01
## [1217]  2.703230e-01  2.689753e-01  2.689364e-01  2.676819e-01
## [1221]  2.666100e-01  2.654794e-01  2.649890e-01  2.637537e-01
## [1225]  2.620433e-01  2.615018e-01  2.609086e-01  2.591229e-01
## [1229]  2.585230e-01  2.578099e-01  2.567541e-01  2.557131e-01
## [1233]  2.551616e-01  2.538789e-01  2.533761e-01  2.507561e-01
## [1237]  2.500999e-01  2.495452e-01  2.491484e-01  2.481058e-01
## [1241]  2.469570e-01  2.463141e-01  2.454776e-01  2.450064e-01
## [1245]  2.444158e-01  2.423361e-01  2.412967e-01  2.403405e-01
## [1249]  2.395703e-01  2.386941e-01  2.383829e-01  2.371193e-01
## [1253]  2.365282e-01  2.355357e-01  2.336962e-01  2.328787e-01
## [1257]  2.315827e-01  2.306909e-01  2.300917e-01  2.294960e-01
## [1261]  2.288149e-01  2.272177e-01  2.260290e-01  2.256677e-01
## [1265]  2.247833e-01  2.242719e-01  2.237551e-01  2.232332e-01
## [1269]  2.215687e-01  2.200130e-01  2.195275e-01  2.194049e-01
## [1273]  2.178256e-01  2.174220e-01  2.159463e-01  2.151868e-01
## [1277]  2.137040e-01  2.126325e-01  2.116409e-01  2.107753e-01
## [1281]  2.102085e-01  2.096221e-01  2.088682e-01  2.086982e-01
## [1285]  2.069568e-01  2.059184e-01  2.058421e-01  2.039737e-01
## [1289]  2.030893e-01  2.030254e-01  2.015040e-01  2.007341e-01
## [1293]  1.988345e-01  1.985683e-01  1.971201e-01  1.956314e-01
## [1297]  1.955677e-01  1.949352e-01  1.940987e-01  1.925923e-01
## [1301]  1.917885e-01  1.906291e-01  1.890493e-01  1.889337e-01
## [1305]  1.881706e-01  1.874987e-01  1.863714e-01  1.859471e-01
## [1309]  1.849835e-01  1.837440e-01  1.823880e-01  1.821247e-01
## [1313]  1.816152e-01  1.803606e-01  1.797152e-01  1.787388e-01
## [1317]  1.782032e-01  1.758232e-01  1.754949e-01  1.742486e-01
## [1321]  1.730597e-01  1.724729e-01  1.722967e-01  1.711633e-01
## [1325]  1.704881e-01  1.691094e-01  1.677707e-01  1.666587e-01
## [1329]  1.662287e-01  1.646221e-01  1.640545e-01  1.632878e-01
## [1333]  1.630307e-01  1.615776e-01  1.602008e-01  1.593602e-01
## [1337]  1.587842e-01  1.576127e-01  1.570318e-01  1.558308e-01
## [1341]  1.553056e-01  1.544207e-01  1.536310e-01  1.532356e-01
## [1345]  1.524192e-01  1.519588e-01  1.508946e-01  1.502777e-01
## [1349]  1.490379e-01  1.470743e-01  1.465671e-01  1.457702e-01
## [1353]  1.453893e-01  1.440984e-01  1.437379e-01  1.422735e-01
## [1357]  1.413315e-01  1.406444e-01  1.400095e-01  1.385854e-01
## [1361]  1.379048e-01  1.372796e-01  1.361358e-01  1.350554e-01
## [1365]  1.345287e-01  1.334900e-01  1.330783e-01  1.320277e-01
## [1369]  1.313733e-01  1.308416e-01  1.292261e-01  1.288141e-01
## [1373]  1.278060e-01  1.269238e-01  1.254617e-01  1.252608e-01
## [1377]  1.246507e-01  1.232218e-01  1.217013e-01  1.214063e-01
## [1381]  1.200538e-01  1.190444e-01  1.183398e-01  1.172134e-01
## [1385]  1.156050e-01  1.147541e-01  1.144292e-01  1.128659e-01
## [1389]  1.124361e-01  1.113008e-01  1.106905e-01  1.102256e-01
## [1393]  1.091058e-01  1.080559e-01  1.070534e-01  1.066772e-01
## [1397]  1.057320e-01  1.042491e-01  1.038259e-01  1.030304e-01
## [1401]  1.020523e-01  1.010558e-01  1.001278e-01  9.999997e-02
## [1405]  9.871498e-02  9.789208e-02  9.708583e-02  9.632919e-02
## [1409]  9.614336e-02  9.554742e-02  9.525529e-02  9.399243e-02
## [1413]  9.205591e-02  9.150988e-02  9.079682e-02  8.995701e-02
## [1417]  8.921291e-02  8.824793e-02  8.746952e-02  8.593175e-02
## [1421]  8.555635e-02  8.532795e-02  8.346277e-02  8.257158e-02
## [1425]  8.212237e-02  8.137034e-02  8.040089e-02  8.030656e-02
## [1429]  7.927977e-02  7.830562e-02  7.771284e-02  7.669069e-02
## [1433]  7.599467e-02  7.530567e-02  7.451922e-02  7.442575e-02
## [1437]  7.250913e-02  7.156089e-02  7.126057e-02  7.003729e-02
## [1441]  6.963760e-02  6.846177e-02  6.732232e-02  6.699991e-02
## [1445]  6.540650e-02  6.507467e-02  6.417920e-02  6.276691e-02
## [1449]  6.189196e-02  6.178276e-02  6.124702e-02  6.042555e-02
## [1453]  6.011628e-02  5.842887e-02  5.811253e-02  5.770996e-02
## [1457]  5.685035e-02  5.588169e-02  5.558905e-02  5.401184e-02
## [1461]  5.312899e-02  5.208132e-02  5.117418e-02  5.104701e-02
## [1465]  5.026862e-02  4.971489e-02  4.921976e-02  4.702415e-02
## [1469]  4.618210e-02  4.587206e-02  4.385873e-02  4.370564e-02
## [1473]  4.320264e-02  4.178571e-02  3.990688e-02  3.929633e-02
## [1477]  3.892000e-02  3.822949e-02  3.772134e-02  3.664748e-02
## [1481]  3.522583e-02  3.479779e-02  3.415034e-02  3.343584e-02
## [1485]  3.277484e-02  3.231955e-02  3.103882e-02  3.047480e-02
## [1489]  2.974159e-02  2.881764e-02  2.810620e-02  2.757020e-02
## [1493]  2.653711e-02  2.614573e-02  2.557157e-02  2.414145e-02
## [1497]  2.375684e-02  2.182326e-02  2.104061e-02  2.016627e-02
## [1501]  1.952740e-02  1.889805e-02  1.813935e-02  1.737330e-02
## [1505]  1.715065e-02  1.556056e-02  1.412035e-02  1.323045e-02
## [1509]  1.255398e-02  1.185841e-02  1.087004e-02  1.070259e-02
## [1513]  8.579872e-03  8.374325e-03  7.159169e-03  6.041210e-03
## [1517]  5.105640e-03  4.336922e-03  3.884967e-03  2.432211e-03
## [1521]  2.199388e-03  1.165057e-03  6.839318e-04 -2.031058e-14
## [1525] -4.230146e-04 -8.119518e-04 -1.398495e-03 -2.799563e-03
## [1529] -3.127780e-03 -4.109185e-03 -4.658540e-03 -5.280037e-03
## [1533] -6.144996e-03 -6.605716e-03 -6.827923e-03 -7.965035e-03
## [1537] -8.981387e-03 -9.318568e-03 -9.661836e-03 -1.167455e-02
## [1541] -1.228408e-02 -1.257863e-02 -1.379968e-02 -1.424007e-02
## [1545] -1.544917e-02 -1.638875e-02 -1.749363e-02 -1.814399e-02
## [1549] -1.891762e-02 -1.958192e-02 -2.077392e-02 -2.190491e-02
## [1553] -2.264474e-02 -2.308602e-02 -2.403635e-02 -2.431966e-02
## [1557] -2.529867e-02 -2.622637e-02 -2.711218e-02 -2.806654e-02
## [1561] -2.887651e-02 -2.965217e-02 -3.008670e-02 -3.083255e-02
## [1565] -3.173257e-02 -3.282051e-02 -3.305202e-02 -3.358802e-02
## [1569] -3.423512e-02 -3.572438e-02 -3.629587e-02 -3.742851e-02
## [1573] -3.798089e-02 -3.928527e-02 -4.041990e-02 -4.127283e-02
## [1577] -4.191747e-02 -4.247974e-02 -4.315054e-02 -4.362320e-02
## [1581] -4.544339e-02 -4.562467e-02 -4.644750e-02 -4.704507e-02
## [1585] -4.774981e-02 -4.836899e-02 -4.855776e-02 -4.894809e-02
## [1589] -4.930892e-02 -5.159670e-02 -5.262291e-02 -5.350768e-02
## [1593] -5.445369e-02 -5.532854e-02 -5.649021e-02 -5.780863e-02
## [1597] -5.796670e-02 -5.874878e-02 -5.947026e-02 -6.028390e-02
## [1601] -6.075567e-02 -6.141152e-02 -6.211871e-02 -6.337977e-02
## [1605] -6.370947e-02 -6.472525e-02 -6.545775e-02 -6.648843e-02
## [1609] -6.683030e-02 -6.790696e-02 -6.842118e-02 -6.948296e-02
## [1613] -7.072388e-02 -7.077007e-02 -7.214381e-02 -7.401438e-02
## [1617] -7.429393e-02 -7.475699e-02 -7.565848e-02 -7.613555e-02
## [1621] -7.733859e-02 -7.751437e-02 -7.808981e-02 -7.937123e-02
## [1625] -8.049696e-02 -8.137947e-02 -8.171345e-02 -8.254249e-02
## [1629] -8.392501e-02 -8.420250e-02 -8.519134e-02 -8.543039e-02
## [1633] -8.711594e-02 -8.732949e-02 -8.753602e-02 -8.947784e-02
## [1637] -8.966622e-02 -9.009860e-02 -9.074744e-02 -9.172004e-02
## [1641] -9.314211e-02 -9.387763e-02 -9.419092e-02 -9.556312e-02
## [1645] -9.667404e-02 -9.693787e-02 -9.736123e-02 -9.791264e-02
## [1649] -9.944972e-02 -9.952987e-02 -1.015384e-01 -1.019349e-01
## [1653] -1.024735e-01 -1.031263e-01 -1.047205e-01 -1.055582e-01
## [1657] -1.057936e-01 -1.065476e-01 -1.069676e-01 -1.082054e-01
## [1661] -1.085462e-01 -1.097069e-01 -1.104312e-01 -1.118193e-01
## [1665] -1.123152e-01 -1.128147e-01 -1.142206e-01 -1.145056e-01
## [1669] -1.147601e-01 -1.159023e-01 -1.166494e-01 -1.175089e-01
## [1673] -1.182373e-01 -1.186912e-01 -1.189698e-01 -1.208044e-01
## [1677] -1.219271e-01 -1.227802e-01 -1.232563e-01 -1.242693e-01
## [1681] -1.249715e-01 -1.254673e-01 -1.259009e-01 -1.266737e-01
## [1685] -1.268749e-01 -1.281204e-01 -1.292794e-01 -1.299136e-01
## [1689] -1.311100e-01 -1.316783e-01 -1.323067e-01 -1.330200e-01
## [1693] -1.345035e-01 -1.348455e-01 -1.355807e-01 -1.359720e-01
## [1697] -1.370761e-01 -1.376114e-01 -1.390457e-01 -1.393793e-01
## [1701] -1.400964e-01 -1.407575e-01 -1.422022e-01 -1.425205e-01
## [1705] -1.439230e-01 -1.440412e-01 -1.443748e-01 -1.453993e-01
## [1709] -1.468065e-01 -1.475211e-01 -1.480238e-01 -1.486845e-01
## [1713] -1.490770e-01 -1.501799e-01 -1.517138e-01 -1.524676e-01
## [1717] -1.531508e-01 -1.540507e-01 -1.549623e-01 -1.557854e-01
## [1721] -1.561853e-01 -1.571231e-01 -1.577934e-01 -1.580965e-01
## [1725] -1.583558e-01 -1.594044e-01 -1.603051e-01 -1.605288e-01
## [1729] -1.624720e-01 -1.630005e-01 -1.645011e-01 -1.649574e-01
## [1733] -1.661771e-01 -1.666271e-01 -1.667356e-01 -1.674311e-01
## [1737] -1.684744e-01 -1.687854e-01 -1.705487e-01 -1.706426e-01
## [1741] -1.713052e-01 -1.722097e-01 -1.731408e-01 -1.741996e-01
## [1745] -1.749493e-01 -1.754516e-01 -1.758493e-01 -1.766626e-01
## [1749] -1.770392e-01 -1.774679e-01 -1.785130e-01 -1.801248e-01
## [1753] -1.812132e-01 -1.816960e-01 -1.821209e-01 -1.825803e-01
## [1757] -1.839924e-01 -1.846787e-01 -1.848298e-01 -1.860195e-01
## [1761] -1.863928e-01 -1.870720e-01 -1.885345e-01 -1.886855e-01
## [1765] -1.888848e-01 -1.900687e-01 -1.912903e-01 -1.918764e-01
## [1769] -1.933042e-01 -1.936480e-01 -1.950570e-01 -1.960854e-01
## [1773] -1.966140e-01 -1.975293e-01 -1.982352e-01 -1.990229e-01
## [1777] -1.996601e-01 -2.002876e-01 -2.009644e-01 -2.017878e-01
## [1781] -2.024498e-01 -2.029503e-01 -2.032360e-01 -2.047213e-01
## [1785] -2.050666e-01 -2.056971e-01 -2.063476e-01 -2.073152e-01
## [1789] -2.085066e-01 -2.101397e-01 -2.106306e-01 -2.118385e-01
## [1793] -2.123576e-01 -2.125969e-01 -2.133358e-01 -2.138561e-01
## [1797] -2.147336e-01 -2.152546e-01 -2.161518e-01 -2.167686e-01
## [1801] -2.182119e-01 -2.187287e-01 -2.188540e-01 -2.201696e-01
## [1805] -2.212003e-01 -2.215987e-01 -2.222074e-01 -2.230315e-01
## [1809] -2.239366e-01 -2.252825e-01 -2.259659e-01 -2.264439e-01
## [1813] -2.268132e-01 -2.274636e-01 -2.283000e-01 -2.293217e-01
## [1817] -2.302241e-01 -2.309631e-01 -2.314201e-01 -2.330442e-01
## [1821] -2.333843e-01 -2.343750e-01 -2.357779e-01 -2.361988e-01
## [1825] -2.369631e-01 -2.374113e-01 -2.384186e-01 -2.386173e-01
## [1829] -2.388880e-01 -2.397496e-01 -2.409983e-01 -2.414714e-01
## [1833] -2.422519e-01 -2.433564e-01 -2.438041e-01 -2.443840e-01
## [1837] -2.448439e-01 -2.461963e-01 -2.469126e-01 -2.473234e-01
## [1841] -2.478488e-01 -2.490379e-01 -2.502219e-01 -2.511128e-01
## [1845] -2.515687e-01 -2.520231e-01 -2.527000e-01 -2.539644e-01
## [1849] -2.552405e-01 -2.562882e-01 -2.571050e-01 -2.574154e-01
## [1853] -2.591434e-01 -2.593760e-01 -2.605483e-01 -2.607586e-01
## [1857] -2.612510e-01 -2.623397e-01 -2.628138e-01 -2.635598e-01
## [1861] -2.646101e-01 -2.656132e-01 -2.659601e-01 -2.663124e-01
## [1865] -2.677312e-01 -2.677644e-01 -2.689670e-01 -2.695556e-01
## [1869] -2.704014e-01 -2.716002e-01 -2.720225e-01 -2.727114e-01
## [1873] -2.739965e-01 -2.747298e-01 -2.754300e-01 -2.760016e-01
## [1877] -2.766372e-01 -2.770582e-01 -2.780530e-01 -2.782592e-01
## [1881] -2.789966e-01 -2.793964e-01 -2.805097e-01 -2.812598e-01
## [1885] -2.820475e-01 -2.827542e-01 -2.835837e-01 -2.844115e-01
## [1889] -2.853167e-01 -2.857684e-01 -2.872769e-01 -2.880456e-01
## [1893] -2.887668e-01 -2.891568e-01 -2.903054e-01 -2.912273e-01
## [1897] -2.917815e-01 -2.918663e-01 -2.928759e-01 -2.935067e-01
## [1901] -2.944850e-01 -2.952613e-01 -2.960500e-01 -2.967903e-01
## [1905] -2.972110e-01 -2.982693e-01 -2.987268e-01 -2.994339e-01
## [1909] -3.004963e-01 -3.010695e-01 -3.018469e-01 -3.029222e-01
## [1913] -3.038979e-01 -3.043997e-01 -3.060321e-01 -3.076944e-01
## [1917] -3.078806e-01 -3.091613e-01 -3.095886e-01 -3.102634e-01
## [1921] -3.104145e-01 -3.118048e-01 -3.120806e-01 -3.126356e-01
## [1925] -3.132510e-01 -3.142126e-01 -3.151195e-01 -3.159123e-01
## [1929] -3.173174e-01 -3.176247e-01 -3.183998e-01 -3.188336e-01
## [1933] -3.199444e-01 -3.207991e-01 -3.211598e-01 -3.217545e-01
## [1937] -3.225180e-01 -3.237549e-01 -3.242403e-01 -3.256206e-01
## [1941] -3.258592e-01 -3.263629e-01 -3.279132e-01 -3.285490e-01
## [1945] -3.286468e-01 -3.298627e-01 -3.307947e-01 -3.319544e-01
## [1949] -3.326558e-01 -3.333278e-01 -3.335751e-01 -3.343199e-01
## [1953] -3.345676e-01 -3.360647e-01 -3.372735e-01 -3.377613e-01
## [1957] -3.381690e-01 -3.395970e-01 -3.410606e-01 -3.413984e-01
## [1961] -3.419704e-01 -3.433232e-01 -3.438023e-01 -3.445813e-01
## [1965] -3.455479e-01 -3.462429e-01 -3.466129e-01 -3.470961e-01
## [1969] -3.476023e-01 -3.481459e-01 -3.490847e-01 -3.494907e-01
## [1973] -3.512657e-01 -3.520059e-01 -3.524568e-01 -3.530788e-01
## [1977] -3.540316e-01 -3.542684e-01 -3.558897e-01 -3.562244e-01
## [1981] -3.570242e-01 -3.576999e-01 -3.579036e-01 -3.590149e-01
## [1985] -3.605056e-01 -3.609581e-01 -3.617777e-01 -3.624385e-01
## [1989] -3.628114e-01 -3.644651e-01 -3.645640e-01 -3.653860e-01
## [1993] -3.657145e-01 -3.664747e-01 -3.673609e-01 -3.681807e-01
## [1997] -3.684372e-01 -3.691701e-01 -3.699906e-01 -3.710383e-01
## [2001] -3.712910e-01 -3.723091e-01 -3.730085e-01 -3.740178e-01
## [2005] -3.749914e-01 -3.751560e-01 -3.765993e-01 -3.767800e-01
## [2009] -3.780466e-01 -3.785375e-01 -3.797956e-01 -3.806310e-01
## [2013] -3.811759e-01 -3.816665e-01 -3.821151e-01 -3.834564e-01
## [2017] -3.838433e-01 -3.848755e-01 -3.856436e-01 -3.862280e-01
## [2021] -3.873022e-01 -3.880978e-01 -3.890219e-01 -3.897280e-01
## [2025] -3.903626e-01 -3.909279e-01 -3.923421e-01 -3.930570e-01
## [2029] -3.940607e-01 -3.944609e-01 -3.949789e-01 -3.953052e-01
## [2033] -3.965983e-01 -3.969509e-01 -3.981739e-01 -3.987932e-01
## [2037] -3.995885e-01 -4.005164e-01 -4.017165e-01 -4.022076e-01
## [2041] -4.027627e-01 -4.031603e-01 -4.038674e-01 -4.044616e-01
## [2045] -4.056573e-01 -4.062606e-01 -4.067053e-01 -4.070722e-01
## [2049] -4.093734e-01 -4.097644e-01 -4.115672e-01 -4.120845e-01
## [2053] -4.130280e-01 -4.138620e-01 -4.147550e-01 -4.155164e-01
## [2057] -4.156412e-01 -4.167688e-01 -4.177598e-01 -4.185643e-01
## [2061] -4.188417e-01 -4.197213e-01 -4.203551e-01 -4.215160e-01
## [2065] -4.219671e-01 -4.228588e-01 -4.236284e-01 -4.243981e-01
## [2069] -4.259038e-01 -4.268304e-01 -4.275597e-01 -4.281935e-01
## [2073] -4.291643e-01 -4.295545e-01 -4.305679e-01 -4.312809e-01
## [2077] -4.319896e-01 -4.325495e-01 -4.332518e-01 -4.342641e-01
## [2081] -4.351759e-01 -4.366910e-01 -4.369782e-01 -4.379122e-01
## [2085] -4.387744e-01 -4.393820e-01 -4.403994e-01 -4.413486e-01
## [2089] -4.416818e-01 -4.420186e-01 -4.438834e-01 -4.445531e-01
## [2093] -4.455116e-01 -4.455791e-01 -4.461412e-01 -4.467685e-01
## [2097] -4.482707e-01 -4.489459e-01 -4.504225e-01 -4.507802e-01
## [2101] -4.514518e-01 -4.526304e-01 -4.530378e-01 -4.533102e-01
## [2105] -4.536316e-01 -4.552307e-01 -4.560327e-01 -4.564167e-01
## [2109] -4.575859e-01 -4.584715e-01 -4.589243e-01 -4.593881e-01
## [2113] -4.611816e-01 -4.617848e-01 -4.621109e-01 -4.635215e-01
## [2117] -4.637319e-01 -4.649412e-01 -4.660097e-01 -4.666032e-01
## [2121] -4.683996e-01 -4.687113e-01 -4.699086e-01 -4.702954e-01
## [2125] -4.706131e-01 -4.713324e-01 -4.726596e-01 -4.730158e-01
## [2129] -4.739996e-01 -4.745021e-01 -4.747593e-01 -4.753106e-01
## [2133] -4.765290e-01 -4.768966e-01 -4.781446e-01 -4.784540e-01
## [2137] -4.805348e-01 -4.806300e-01 -4.815975e-01 -4.818597e-01
## [2141] -4.824762e-01 -4.835574e-01 -4.844090e-01 -4.850485e-01
## [2145] -4.858033e-01 -4.864587e-01 -4.878550e-01 -4.887003e-01
## [2149] -4.893434e-01 -4.897769e-01 -4.903290e-01 -4.919455e-01
## [2153] -4.927460e-01 -4.933990e-01 -4.937928e-01 -4.944956e-01
## [2157] -4.955127e-01 -4.969867e-01 -4.974580e-01 -4.977362e-01
## [2161] -4.987917e-01 -4.996403e-01 -5.001491e-01 -5.015991e-01
## [2165] -5.026221e-01 -5.026487e-01 -5.030123e-01 -5.038236e-01
## [2169] -5.046579e-01 -5.052221e-01 -5.058748e-01 -5.071322e-01
## [2173] -5.081757e-01 -5.087532e-01 -5.092757e-01 -5.106414e-01
## [2177] -5.116181e-01 -5.130418e-01 -5.136939e-01 -5.139768e-01
## [2181] -5.149230e-01 -5.157644e-01 -5.167315e-01 -5.173506e-01
## [2185] -5.179808e-01 -5.192564e-01 -5.198985e-01 -5.205604e-01
## [2189] -5.210389e-01 -5.219094e-01 -5.237250e-01 -5.244022e-01
## [2193] -5.251531e-01 -5.257431e-01 -5.261199e-01 -5.278237e-01
## [2197] -5.283582e-01 -5.288686e-01 -5.301761e-01 -5.307216e-01
## [2201] -5.314041e-01 -5.321364e-01 -5.332981e-01 -5.337170e-01
## [2205] -5.352044e-01 -5.355551e-01 -5.363639e-01 -5.379508e-01
## [2209] -5.388093e-01 -5.391346e-01 -5.398563e-01 -5.407093e-01
## [2213] -5.422964e-01 -5.426992e-01 -5.438032e-01 -5.441051e-01
## [2217] -5.446635e-01 -5.451943e-01 -5.465968e-01 -5.487743e-01
## [2221] -5.490353e-01 -5.492435e-01 -5.495100e-01 -5.504454e-01
## [2225] -5.514165e-01 -5.517404e-01 -5.525484e-01 -5.535413e-01
## [2229] -5.539108e-01 -5.550694e-01 -5.566398e-01 -5.572176e-01
## [2233] -5.578218e-01 -5.583686e-01 -5.594805e-01 -5.601054e-01
## [2237] -5.608373e-01 -5.616051e-01 -5.631092e-01 -5.644129e-01
## [2241] -5.651926e-01 -5.661513e-01 -5.673524e-01 -5.685688e-01
## [2245] -5.690842e-01 -5.692358e-01 -5.706308e-01 -5.711612e-01
## [2249] -5.719270e-01 -5.723392e-01 -5.735043e-01 -5.752621e-01
## [2253] -5.754210e-01 -5.765653e-01 -5.772080e-01 -5.785331e-01
## [2257] -5.791137e-01 -5.797773e-01 -5.808732e-01 -5.821094e-01
## [2261] -5.824576e-01 -5.839129e-01 -5.851245e-01 -5.856240e-01
## [2265] -5.861768e-01 -5.871592e-01 -5.877132e-01 -5.888483e-01
## [2269] -5.895338e-01 -5.907976e-01 -5.914093e-01 -5.921470e-01
## [2273] -5.930698e-01 -5.936209e-01 -5.951842e-01 -5.959337e-01
## [2277] -5.967312e-01 -5.973795e-01 -5.983511e-01 -5.990959e-01
## [2281] -5.999299e-01 -6.004060e-01 -6.012562e-01 -6.024601e-01
## [2285] -6.041318e-01 -6.050674e-01 -6.057360e-01 -6.062001e-01
## [2289] -6.065177e-01 -6.069434e-01 -6.071043e-01 -6.090072e-01
## [2293] -6.099800e-01 -6.103601e-01 -6.109699e-01 -6.116382e-01
## [2297] -6.131860e-01 -6.141004e-01 -6.152182e-01 -6.159240e-01
## [2301] -6.164654e-01 -6.180394e-01 -6.190416e-01 -6.196658e-01
## [2305] -6.199507e-01 -6.209031e-01 -6.211483e-01 -6.229232e-01
## [2309] -6.236650e-01 -6.241379e-01 -6.247188e-01 -6.257962e-01
## [2313] -6.267502e-01 -6.268131e-01 -6.282034e-01 -6.299524e-01
## [2317] -6.310863e-01 -6.311515e-01 -6.329138e-01 -6.334025e-01
## [2321] -6.338102e-01 -6.345404e-01 -6.360702e-01 -6.365238e-01
## [2325] -6.382337e-01 -6.393031e-01 -6.397021e-01 -6.412019e-01
## [2329] -6.417144e-01 -6.419779e-01 -6.427420e-01 -6.437669e-01
## [2333] -6.449169e-01 -6.458523e-01 -6.460786e-01 -6.473336e-01
## [2337] -6.492735e-01 -6.500850e-01 -6.508303e-01 -6.511631e-01
## [2341] -6.515116e-01 -6.529589e-01 -6.544999e-01 -6.556987e-01
## [2345] -6.571243e-01 -6.577152e-01 -6.592329e-01 -6.597537e-01
## [2349] -6.610358e-01 -6.616503e-01 -6.626409e-01 -6.636514e-01
## [2353] -6.641971e-01 -6.652550e-01 -6.671378e-01 -6.676899e-01
## [2357] -6.692703e-01 -6.694857e-01 -6.706697e-01 -6.724380e-01
## [2361] -6.727405e-01 -6.743215e-01 -6.752100e-01 -6.760221e-01
## [2365] -6.768598e-01 -6.779530e-01 -6.783145e-01 -6.786225e-01
## [2369] -6.796592e-01 -6.816046e-01 -6.828390e-01 -6.849387e-01
## [2373] -6.855589e-01 -6.862747e-01 -6.866211e-01 -6.874805e-01
## [2377] -6.894088e-01 -6.900872e-01 -6.902377e-01 -6.911336e-01
## [2381] -6.913310e-01 -6.925380e-01 -6.929071e-01 -6.937656e-01
## [2385] -6.950447e-01 -6.972695e-01 -6.981215e-01 -6.985693e-01
## [2389] -6.996407e-01 -7.005504e-01 -7.013571e-01 -7.014390e-01
## [2393] -7.024008e-01 -7.041434e-01 -7.059525e-01 -7.066117e-01
## [2397] -7.080113e-01 -7.084214e-01 -7.094348e-01 -7.099873e-01
## [2401] -7.113718e-01 -7.122500e-01 -7.141076e-01 -7.160960e-01
## [2405] -7.164472e-01 -7.180571e-01 -7.184019e-01 -7.190106e-01
## [2409] -7.202969e-01 -7.207608e-01 -7.219155e-01 -7.238476e-01
## [2413] -7.240643e-01 -7.255736e-01 -7.261882e-01 -7.278316e-01
## [2417] -7.295975e-01 -7.303619e-01 -7.319734e-01 -7.327603e-01
## [2421] -7.341933e-01 -7.352122e-01 -7.363202e-01 -7.366143e-01
## [2425] -7.392421e-01 -7.401556e-01 -7.412756e-01 -7.416491e-01
## [2429] -7.435058e-01 -7.452108e-01 -7.457717e-01 -7.472214e-01
## [2433] -7.480059e-01 -7.485601e-01 -7.503011e-01 -7.510209e-01
## [2437] -7.525909e-01 -7.527406e-01 -7.539309e-01 -7.552691e-01
## [2441] -7.560376e-01 -7.569679e-01 -7.585490e-01 -7.591964e-01
## [2445] -7.597083e-01 -7.613917e-01 -7.633293e-01 -7.644921e-01
## [2449] -7.658849e-01 -7.665955e-01 -7.692665e-01 -7.697936e-01
## [2453] -7.712865e-01 -7.719373e-01 -7.734889e-01 -7.743440e-01
## [2457] -7.746455e-01 -7.757996e-01 -7.772636e-01 -7.784809e-01
## [2461] -7.791978e-01 -7.800147e-01 -7.807378e-01 -7.828205e-01
## [2465] -7.835324e-01 -7.853974e-01 -7.859674e-01 -7.877320e-01
## [2469] -7.886073e-01 -7.896368e-01 -7.911370e-01 -7.934708e-01
## [2473] -7.946376e-01 -7.959537e-01 -7.972540e-01 -7.989390e-01
## [2477] -7.995557e-01 -8.001248e-01 -8.012701e-01 -8.043431e-01
## [2481] -8.052798e-01 -8.063297e-01 -8.080776e-01 -8.088622e-01
## [2485] -8.099070e-01 -8.099939e-01 -8.108457e-01 -8.130204e-01
## [2489] -8.144197e-01 -8.164470e-01 -8.173911e-01 -8.178828e-01
## [2493] -8.198154e-01 -8.203534e-01 -8.208488e-01 -8.237349e-01
## [2497] -8.247875e-01 -8.259379e-01 -8.276664e-01 -8.283327e-01
## [2501] -8.294139e-01 -8.316046e-01 -8.328041e-01 -8.346241e-01
## [2505] -8.347898e-01 -8.358497e-01 -8.371983e-01 -8.406719e-01
## [2509] -8.416732e-01 -8.424827e-01 -8.434863e-01 -8.440333e-01
## [2513] -8.468341e-01 -8.488828e-01 -8.508509e-01 -8.520893e-01
## [2517] -8.533449e-01 -8.546998e-01 -8.556689e-01 -8.577624e-01
## [2521] -8.586040e-01 -8.608005e-01 -8.621018e-01 -8.635502e-01
## [2525] -8.643375e-01 -8.664099e-01 -8.674922e-01 -8.694648e-01
## [2529] -8.707802e-01 -8.727218e-01 -8.748411e-01 -8.763657e-01
## [2533] -8.767712e-01 -8.790716e-01 -8.792706e-01 -8.807429e-01
## [2537] -8.811582e-01 -8.825494e-01 -8.863650e-01 -8.871929e-01
## [2541] -8.877796e-01 -8.894906e-01 -8.902449e-01 -8.931704e-01
## [2545] -8.947297e-01 -8.963176e-01 -8.978468e-01 -9.007862e-01
## [2549] -9.010477e-01 -9.023308e-01 -9.039401e-01 -9.049972e-01
## [2553] -9.081756e-01 -9.088427e-01 -9.097581e-01 -9.130090e-01
## [2557] -9.138557e-01 -9.147078e-01 -9.169336e-01 -9.194737e-01
## [2561] -9.215254e-01 -9.256328e-01 -9.275025e-01 -9.292914e-01
## [2565] -9.302889e-01 -9.318631e-01 -9.333906e-01 -9.335973e-01
## [2569] -9.372195e-01 -9.377511e-01 -9.403134e-01 -9.438322e-01
## [2573] -9.444302e-01 -9.471916e-01 -9.490129e-01 -9.493538e-01
## [2577] -9.499172e-01 -9.526532e-01 -9.540852e-01 -9.579994e-01
## [2581] -9.607663e-01 -9.614945e-01 -9.649722e-01 -9.675300e-01
## [2585] -9.692904e-01 -9.710550e-01 -9.730043e-01 -9.767997e-01
## [2589] -9.782272e-01 -9.807540e-01 -9.830686e-01 -9.838844e-01
## [2593] -9.849619e-01 -9.882306e-01 -9.914207e-01 -9.925473e-01
## [2597] -9.961299e-01 -9.978098e-01 -1.001370e+00 -1.002539e+00
## [2601] -1.004439e+00 -1.007577e+00 -1.013514e+00 -1.016435e+00
## [2605] -1.020195e+00 -1.021618e+00 -1.024860e+00 -1.026578e+00
## [2609] -1.030787e+00 -1.033689e+00 -1.034819e+00 -1.035902e+00
## [2613] -1.039176e+00 -1.046242e+00 -1.047559e+00 -1.052570e+00
## [2617] -1.053755e+00 -1.056640e+00 -1.063140e+00 -1.064263e+00
## [2621] -1.067424e+00 -1.069177e+00 -1.072690e+00 -1.076585e+00
## [2625] -1.077383e+00 -1.080870e+00 -1.081678e+00 -1.085491e+00
## [2629] -1.092708e+00 -1.096814e+00 -1.098150e+00 -1.105355e+00
## [2633] -1.107815e+00 -1.108830e+00 -1.114437e+00 -1.121841e+00
## [2637] -1.125104e+00 -1.126824e+00 -1.131623e+00 -1.136020e+00
## [2641] -1.138336e+00 -1.145822e+00 -1.147700e+00 -1.149313e+00
## [2645] -1.156086e+00 -1.159219e+00 -1.164566e+00 -1.169724e+00
## [2649] -1.174185e+00 -1.178681e+00 -1.188530e+00 -1.195898e+00
## [2653] -1.202131e+00 -1.208370e+00 -1.210393e+00 -1.217010e+00
## [2657] -1.225796e+00 -1.237729e+00 -1.244483e+00 -1.251114e+00
## [2661] -1.256676e+00 -1.265003e+00 -1.283626e+00 -1.289836e+00
## [2665] -1.314513e+00 -1.335519e+00 -1.380938e+00
## 
## $x
## NULL
## 
## $ac
## [1] 0
## 
## $GOF
## [1] 0.02803631 0.03628889

Falta acrescentar Legenda

Plot do modelo rpart - Recursive Partitioning and Regression Trees - personalizando automaticamente a partir do gráfico para o tipo de resposta do modelo.

rp <- rpart::rpart(formula = WR_Grp~.,data=test2)
#rpart::plotcp(rp)
rpart.plot(rp)

rpart.plot.version1(rp)

Falta acrescentar Legenda

5 Conclusão e Considerações

O Random Forest apresenta overfiting para os dados de treinamento, como é possível observar na matriz de confusão obtida, e a partir de 150 arvores de regressão no algoritmo, tem-se um ganho minimo ao adicionar novas árvores.

É possível comparar nossos resultados com os resultados da usuária Yueming (disponíveis no site Kaggle clicando AQUI e AQUI), sendo que a partir de seus resultados é possível obter algumas sugestões de melhora do nosso algoritmo, principalmente quanto ao tratamento dos dados (NA e zeros). Em nosso algoritmo, inicialmente, os filmes repetidos não haviam sido considerados, a autora observou 43 observações com repetição e variáveis com excesso de respostas nulas. Quanto as observações repetidas, replicamos o passo sugerido por ela e foram excluídos todos os filmes com repetição.

Em relação as respostas nulas em excesso, ela tratou essas, como NA, entretanto durante as etapas de preparação de dados, descobrimos que a única variável com tal problema era a “movie_facebook_likes” que acabou sendo excluída de nossa análise.

Ainda foram observadas poucos filmes antes de 1980. Tal situação foi enfrentada pela autora com a exclusãos dessas observações raras. Replicamos esse passo também. De uma forma geral, a autora comparou três modelos (KNN, Árvore de Decisão e Random Forest) apenas levando em consideração a acurácia obtida nos modelos.

Aqui, tentamos levar em consideração os valores, principalmente, da matriz de confusão e não somente a acurácia. E podemos concluir que:

Base de dados [0, 6.5) [6.5, 10]
Movie (treino + teste) 1883 1883
Treino 1328 1339
Teste 555 544

Ainda conseguimos obter quais são os fatores/variáveis mais importantes para análise de classificação, dentre as variáveis existentes. Abaixo citamos as top 10 variáveis mais importantes.

RF1 = RF1[1:10,]
hc6_1 <- highchart() %>%
  hc_add_series(data = RF1$importance, 
                type = "bar",
                name = "Importância",
                showInLegend = FALSE,
                tooltip = list(valueDecimals = 2, valuePrefix = "", valueSuffix = ""),
                color="orange") %>%
  hc_yAxis(title = list(text = "Importância"), 
           allowDecimals = TRUE, max = 200,
           labels = list(format = "{value}")) %>%
  hc_xAxis(title = list(text = "Fatores"),
           categories = RF1$variables,
           tickmarkPlacement = "on",
           opposite = FALSE) %>%
  hc_title(text = "Importância por fator - Random Forest",
           style = list(fontWeight = "bold")) %>% 
  hc_subtitle(text = paste("")) %>%
      hc_tooltip(valueDecimals = 2,
                 pointFormat = "Importância: {point.y}")%>%
                 #pointFormat = "Variável: {point.x} <br> Importância: {point.y}") 
      hc_credits(enabled = TRUE, 
                 text = "Fonte: IMDB/KAGGLE. Elaboração: Ewerson Pimenta.",
                 style = list(fontSize = "10px")) %>%
  hc_exporting(enabled = TRUE, filename = "F6_1-importance-Pimenta")
#hc <- hc %>% 
#  hc_add_theme(hc_theme_darkunica())
hc6_1

Tempo de execução

proc.time() -ptm
##    user  system elapsed 
##   60.16    1.93   65.76